Guidelines for Using Building Information Modeling (BIM) for Environmental Analysis of Buildings

MISSING IMAGE

Material Information

Title:
Guidelines for Using Building Information Modeling (BIM) for Environmental Analysis of Buildings
Physical Description:
1 online resource (134 p.)
Language:
english
Creator:
Reeves, Thomas John
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.S.B.C.)
Degree Grantor:
University of Florida
Degree Disciplines:
Building Construction
Committee Chair:
Olbina, Svetlana
Committee Co-Chair:
Issa, R. Raymond
Committee Members:
Srinivasan, Ravi

Subjects

Subjects / Keywords:
bim -- simulation -- sustainability
Building Construction -- Dissertations, Academic -- UF
Genre:
Building Construction thesis, M.S.B.C.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of high-performance buildings.  Building Energy Modeling (BEM), a subset of BIM, employs various simulation tools for predicting the environmental performance of buildings.  As the demand for high-performance buildings has increased, BEM has facilitated the delivery of buildings that meet expected performance requirements. The research objectives were to: 1) evaluate various BEM tools, and 2) develop guidelines for using BEM tools in design and delivery of high-performance buildings.  Twelve BEM tools were evaluated using four criteria: interoperability, user-friendliness, available inputs, and available outputs.  The top three programs were selected based on this evaluation and used in the case study to simulate energy consumption, daylighting, and natural ventilation fortwo buildings, one LEED certified and one non-LEED certified. The results of the case study were used to compare the environmental performance of the two buildings and to develop guidelines for using BEM tools to analyze building environmental performance.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Thomas John Reeves.
Thesis:
Thesis (M.S.B.C.)--University of Florida, 2012.
Local:
Adviser: Olbina, Svetlana.
Local:
Co-adviser: Issa, R. Raymond.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-08-31

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2012
System ID:
UFE0044684:00001


This item is only available as the following downloads:


Full Text

PAGE 1

GUIDELINES FOR USING BUILDING INFORMATION MODELING (BIM) FOR ENVIRONMENTAL ANALYSIS OF BUILDINGS By THOMAS J. REEVES A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUILDING CONSTRUCTION UNIVERSITY OF FLORIDA 2012 1

PAGE 2

2012 Thomas J. Reeves 2

PAGE 3

To my parents, Frances and Westley Reeves, and my brother, Lary Reeves 3

PAGE 4

ACKNOWLEDGMENTS First and foremost, I would like to thank my thesis committee members, Dr. Svetlana Olbina, Dr. Raymond Issa, and Dr. Ravi Srinivasan for their continued insight and the direction that they br ought to this research. Without their passion for research and dedication to BIM and sustainability, this re search could not have progressed to this point. The rigor and knowledge they brought to this process was enormous and I am truly grateful. I would also like to thank the faculty of the Syracuse University School of Architecture for putting my head in the clouds, and the faculty of the Un iversity of Florida M.E. Rinker, Sr. School of Building Cons truction for putting my feet on the ground. Finally, I must thank my family (from Ne w Jersey to the Philippines) for their continued and unwavering support in all of my endeavors. In particular I must thank my mother, father, and brother, whose passion and dedication to their respective fields continues to inspire me. 4

PAGE 5

TABLE OF CONTENTS page ACKNOWLEDG MENTS..................................................................................................4LIST OF TABLES............................................................................................................8LIST OF FI GURES ........................................................................................................10ABSTRACT ...................................................................................................................12CHAPTER 1 INTRODUC TION....................................................................................................131.1 Problem St atement...........................................................................................141.2 Research Objectiv es.........................................................................................141.3 Projec t Scope...................................................................................................162 LITERATURE REVIEW..........................................................................................182.1 Over view ...........................................................................................................182.2 BEM App licati ons..............................................................................................192.2.1 ASHRAE St andard 90. 1.........................................................................212.2.2 Use of BEM in C onceptual Desi gn Phas e..............................................222.2.3 Use of BEM in Desi gn Development Phase...........................................222.2.4 Use of BEM in Cons truction Docume nts Phase.....................................232.2.5 Use of BEM in Constr uction and Contract ing Phas e..............................242.2.6 Use of BEM in Faci lities Management Phase.........................................252.2.7 Integrati ng BEM with BIM.......................................................................252.3BEM Capabi lities...........................................................................................282.3.1 I nputs ......................................................................................................292.3.2 Ou tputs...................................................................................................312.4Existing BEM Tools.......................................................................................342.4.1 Ener gyPlus .........................................................................................342.4.2 eQuest................................................................................................362.4.3 Autode sk Ecotec t................................................................................372.4.4 Autodesk Green Building St udio .........................................................382.4.5 Graphisoft EcoDesigner......................................................................392.4.6 IES (IES )...............................................392.4.7 Bentley Heva comp Simu lator..............................................................402.4.8 Bentley Ta s Simula tor.........................................................................412.4.9 DesignB uilder.....................................................................................422.4.10 E nergy10 ..........................................................................................432.4.11 HE ED.................................................................................................442.4.12 Visual DOE 4.0..................................................................................442.5Limitations of Buildi ng Energy M odeling .......................................................45 5

PAGE 6

3 RESEARCH ME THODOLOGY...............................................................................473.1 Initia l Evaluat ion............................................................................................473.2Case Study....................................................................................................483.3Re-evaluation of BEM Tools Used in the Case St udy...................................513.4Developing Guidelines for BEM Selection and A pplication...........................524 RESULT S...............................................................................................................534.1Initial Eval uation............................................................................................534.1.1 User Fr iendline ss....................................................................................544.1.2 Inter operabilit y........................................................................................564.1.3 Avail able Input s......................................................................................574.1.4 Availabl e Output s...................................................................................584.1.5 Cumulati ve Score...................................................................................594.2Case Study....................................................................................................614.2.1 Ener gy Us age........................................................................................624.2.2 Daylighti ng Perform ance........................................................................644.2.3 Natural Ventilat ion..................................................................................654.3 Re-Evaluation of Building Energy Modeling Tools Used in the Case Study...674.4 Guidelines for using Ecotect, Green Building Studio and IES......764.4.1 Model Preparat ion in Re vit......................................................................774.4.2 Model Preparati on in Building Energy Modeling So ftware.......................774.4.3 Weather Da ta Acquisi tion.......................................................................794.4.4 Schedule Im plementat ion.......................................................................804.4.6 Daylight ing Anal ysis...............................................................................864.4.7 Natural Vent ilation A nalysis ...................................................................884.4.8 Results Analysis in the Building Energy M odeling T ools........................904.5Guidelines for Using Build ing Energy Modeling.............................................914.5.1 Guidelines for Building Energy Modeling Applicat ion.............................924.5.2 Guidelines for Building Ener gy Modeling Softwar e Select ion.................975 CONCLUSIONS AND RE COMMENDATION S.....................................................1015.1Conclusions.................................................................................................1015.1.1 Objective 1: In itial Evalua tion...............................................................1015.1.2 Objective 2: Case St udy.......................................................................1015.1.3 Objective 3: Re-evaluation of BEM Tools Used in the Case Study.......1025.1.4 Objective 4: Developing Guid elines for Using Building Energy Modeling......................................................................................................1035.2Research Limitations...................................................................................1035.2.1 Objective 1: In itial Evalua tion...............................................................1035.2.2 Objective 2: Case St udy.......................................................................1045.2.3 Objective 3: Re-evaluation of t he BEM Tools Used in Case Study.......1065.2.4 Objective 4: Developing Guid elines for Using Building Energy Modeling ......................................................................................................1065.3Recommendations for Fu ture Res earch.....................................................107 6

PAGE 7

APPENDIX A INITIAL EVAL UATION..........................................................................................109B CASE ST UDY.......................................................................................................116C GUIDELINES FOR USING BUILDI NG ENERGY MO DELING .............................127REFERENC ES............................................................................................................131BIOGRAPHICAL SKETCH ..........................................................................................134 7

PAGE 8

LIST OF TABLES Table page 3-1 Comparison of the buildings used in the case study...........................................50 3-2 Profiles of rooms compared for daylighti ng analysi s...........................................51 4-1 Comparison of daylight fact ors for the sele cted rooms.......................................65 4-2 Natural Ventilation Simulation Results for three BEM tools. Potential energy savings from natural ventilation (kWh) ................................................................66 4-3 Re-evaluation matrix with various weightings.....................................................70 4-4 Re-evaluation of three BEM tools for inte roperabili ty..........................................72 4-5 Re-evaluation of three BEM tools for user friendli ness.......................................73 4-6 Re-evaluation of three BEM tools for versatilit y..................................................74 4-7 Re-evaluation of thr ee BEM tools for s peed........................................................76 4-8 BEM tool use during c onceptual desi gn phase...................................................94 4-9 BEM tool use during design developm ent phase................................................94 4-10 BEM tool use during c onstruction docum ents phas e..........................................95 4-11 BEM tool use during constr uction and contra cting phas e...................................95 4-12 BEM tool use during facilities management phase.............................................96 4-13 Recommended required inputs for BEM si mulations in the different building lifecycle pha ses..................................................................................................98 A-1 lnteroperability subcriteria checklist and raw scores......................................110 A-2 User friendliness sub-criter ia checklist and raw scores....................................111 A-3 Available inputs subcriteria checklist and ra w scores .......................................112 A-4 Available outputs che cklist and raw scores......................................................114 A-5 Cumulative score with respective criteria scores..............................................115 B-1 Annual Energy Usage Rinker Hall (output of Green Building Studio simulati on)........................................................................................................ 117 8

PAGE 9

B-2 Annual Energy Usage Gerson Hall (output of Green Building Studio simulati on)........................................................................................................ 118 B-3 Natural Ventilation Gains Rinker Hall (output of Ecotect simulati on).................120 B-4 Natural Ventilation Gains Gerson Hall (output of Ecotec t simulati on)................121 B-5 Natural Ventilation Potential Rinker Hall (output of Gr een Building Studio simulati on)........................................................................................................ 122 B-6 Natural Ventilation Potential Gerson Hall (Output of Gr een Building Studio simulati on)........................................................................................................ 122 C-1 Ecotect Guidelines and Recommendations Matrix..................................128 C-2 Green Building Studio Gu idelines and Recommendations Matrix..............129 C-3 IES Guidelines and Recommendations Matrix..................................130 9

PAGE 10

LIST OF FIGURES Figure page 2-1 Information exchange in building design and delivery workflows. A) Traditional design/delivery. B) BIM-bas ed collaboration. (Source: original).......262-2 Example of BEM data flow ( adapted from US General Services Administra tion 2009) ...........................................................................................283-1 GbXML files of buildings used in the case study exported from Revit Architecture. A) Rinker Hall. B) Ge rson Ha ll...................................................494-1 Initial evaluation scoring system with criteria and subcrite ria.............................544-2 User Friendlines s................................................................................................554-3 Interoper ability....................................................................................................574-4 Availabl e Inputs..................................................................................................584-5 Available Outputs...............................................................................................594-6 Overall scores of the BEM tool initial evaluation.................................................604-7 The scores for available inputs and available outputs of the BEM to ols..............614-8 Energy use intensity (EUI) comparis on by building and by BEM tool. Dotted line denotes the CBECS nati onal median EUI for educational building types (104 kBtu /SF) .....................................................................................................624-9 Energy use breakdown for two buildings used in case study using three BEM tools. ...................................................................................................................634-10 Diagram of building ori entations relative to summe rtime prevailing winds...........674-11 Re-evaluation scoring system wit h criteria and subcrite ria..................................684-12 Re-evaluation un-weighted cumula tive sco res....................................................694-13 Location of weather data for three BEM tools in proximity to case study buildi ngs.............................................................................................................794-14 Ecotect Sc hedule Ed itor..................................................................................814-15 Mean monthly average temperatur es and corresponding comfort ranges. The shaded area refers to acceptable air-c onditioned thermal comfort ranges, and the black lines refer to acceptable the rmal range for natural ventilation. Dotted 10

PAGE 11

lines denote the acceptable thermal co mfort range for given mean monthly outdoor temperatures (ASHRAE 2004)...............................................................824-16 IES schedule ed itor interface..................................................................834-17 IES Modulating formula profile creation interf ace allows schedules to be derived from the rmal paramet ers...................................................................844-18 Green Building Studio r un chart comparing buildings used in case study........854-19 Workflow of energy modeling me thodology employed in case study..................914-20 Guidelines for BEM software sele ction.............................................................100B-1 Rinker Hall energy use breakdown (output of Green Building Studio simulati on)........................................................................................................ 117B-2 Rinker Hall annual fuel use break down (output of Green Building Studio simulati on)........................................................................................................ 118B-3 Gerson Hall energy use breakdown (output of Green Building Studio simulati on)........................................................................................................ 119B-4 Gerson Hall Energy Use Breakdow n (output of Green Building Studio simulati on)........................................................................................................ 119 11

PAGE 12

Abstract Of Thesis Presented To The Graduate School Of The University Of Florida In Partial Fulfillment Of The Requirements For The Degree Of Master Of Science In Building Construction GUIDELINES FOR USING BUILDING INFORMATION MODELING (BIM) FOR ENVIRONMENTAL ANALYSIS OF BUILDINGS By Thomas J. Reeves August 2012 Chair: Svetlana Olbina Cochair: Raymond Issa Major: Building Construction Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of hi gh-performance buildings. Building Energy Modeling (BEM), a subset of BIM, employs vari ous simulation tools for predicting the environmental performance of buildings. As the demand for high-p erformance buildings has increased, BEM has facilit ated the delivery of buildin gs that meet expected performance requirements. The research obje ctives were to: 1) evaluate various BEM tools, and 2) develop guidelines for using BEM tools in des ign and delivery of highperformance buildings. Twelve BEM tools were evaluated using four criteria: interoperability, user-friendliness, available inputs, and available outputs. The top three programs were selected based on this evaluati on and used in the case study to simulate energy consumption, daylight ing, and natural ventilation for two buildings, one LEED certified and one non-LEED certified. The re sults of the case study were used to compare the environmental perfo rmance of the two buildings and to develop guidelines for using BEM tools to analyze bu ilding environmental performance. 12

PAGE 13

CHAPTER 1 INTRODUCTION Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of high-perform ance buildings. Buildi ng Energy Modeling (BEM), a subset of BIM, employs various simulation tools for analyzing the environmental performance of buildings. As the demand for high-p erformance buildings has increased, BEM has facilit ated the delivery of buildin gs that meet expected performance requirements. The development of such tool s has been integral to the process of integrated proj ect delivery which tests and implements green building strategies from design to exec ution. By integrating BEM wit h the specialties of various other team members working around a ce ntralized BIM model (e.g. structural, mechanical, architectural, planning), the pr ocess has the potential to become seamless. As sustainability becomes a standard prac tice in the buildi ng industry, the demand for high-performance buildings increases. Go als related to sustainability are being set ever higher, demanding greater levels of energy and resource efficiency (Bringezu, 2002). With the demand for high performance buildings and the resu lting challenges posed to designers and builders, the integrat ion of building performance analyses into the design and construction process becomes crucial. BIM in conjunction with BEM seeks to make this integration seamle ss throughout the design process (US General Services Administration 2005). BEM allows design professiona ls to predict how well a building will perform upon completion and provides greater insurance t hat designs will meet or exceed intended performance requirements (Krygiel & Nies 2 008). By allowing design professionals to simulate building performance in a virtual environment, BEM tools provide feedback 13

PAGE 14

related to environmental responsiveness throughout the design proc ess (Schlueter & Thesseling 2009). The integration of BEM tool s into design not only provides greater certainty to designers and owners of a buildi ngs performance, but also aids in the design and construction of greener buildings. The use of BEM tools in the architecture, engineering, and construction (AEC) industry has proven beneficial to both improve building performance and to demonstrate ener gy efficiency to sustainability rating systems like LEED. 1.1 Problem Statement While the building sector comprises only 8% of the United St ates gross domestic product, it is responsible for 40% of US ener gy consumption (US Department of Energy 2007) and 38% of carbon dioxide emissions (US Green Building Council 2007). The development of building energy modeling and its integration into the design and operation of the built environment could contribute to lowe ring these figures in one of the most critical sectors for su stainability. Aside from the moral obligations related to sustainability, the legal obligatio ns of parties aiming to achi eve a LEED certified building make building energy modeling all the more necessary. There are currently several existing BEM tools available for use in the AEC industry, and there is a need to investigate and evaluate how these various tools can be employed. 1.2 Research Objectives This research aimed to develop a set of guidelines and recommendations for using building energy modeling for the analysis of high performance buildings. In particular, the research focused on the building perfo rmance parameters of w hole-building energy use, daylighting, and natural ventilation potential. Intended us ers of the guidelines and recommendations are building designer s and green building consultants. 14

PAGE 15

The purpose of the research was to eval uate some of the most widely used BEM tools in the US and to provide potential BEM users with recommendations in the selection and utilization of a BEM tool. Different BEM tools are des igned for different applications and have varying learning curv es, capabilities, and degrees of accuracy. The research cross evaluated these BEM tool s using a variety of criteria, and assessed the application of the top thr ee tools to aide potential BEM users in the selection and integration of a BEM tool in to building design and delivery. There were four primar y research objectives: I. Initial evaluation of 12 BEM tools via literature review II. Investigation of the top thr ee BEM tools through a case study III. Re-evaluation of t he top three BEM tools us ed in the case study IV. Developing a set of guidelines for using BEM for environmental analysis of buildings The first project objective was to evaluate 12 major building energy modeling (BEM) tools to identify the top three. In this stage the following BEM tools were compared: Graphisoft EcoDesigner, Bentley Tas Simulator V8i, Bentley Hevacomp Simulator V8i, Autodesk Ecotect Autodesk Green Building Studio, DesignBuilder, Visual DOE 4.0, Energy10, EnergyPlus, E-Quest and HEED. The cross evaluation was then used to select t he top three BEM tools based on the identified criteria. The top three BEM tools were selected to continue to the second phase of the research and the second objective, which consis ted of utilizing each simulation tool in a case study. The case study was comprised of two comparisons. First the research compared the analyses and simulations of t he three programs for two buildings; one 15

PAGE 16

LEED certified (Rinker Hall) and one non-LEED certified (Gerson Ha ll). Secondly, the case study also compared the results of each simulation for each of the three BEM tools used. Each BEM tool is used to simulate each buildings performance in three areas of building performance: energy usage, daylighting, and natural ventilation. The third objective of the research was to select the strongest software based on the criteria for evaluation. In this stage, a matrix was developed and used to reevaluate the software with various weightings assi gned to the criteria for evaluation. The fourth objective of the research wa s to develop guidelines for using BEM. The guidelines were meant to help potential BEM users both in t he selection of a BEM tool and in BEM application. Gui delines were based on observations throughout the case studys energy modeling process and were organized by building lifecycle phase application. 1.3 Project Scope The overall aim of this research is to integrate BEM t ools for environmental analysis into the process of the design and construction of high-performance buildings. In order to achieve this aim, guidelines and recommendations for the use and application of BEM tools for the environmental analysis of buildings were developed. In the first phase, the project focused on the ev aluation of existing BEM tools. The three most appropriate BEM tools were selected. The second phase consisted of the case study. The BIM models for the two buildings (LEED certified and non-LEED certified) were developed. Simulations of the environmental performance of these two buildings were conducted using each of the three softwa re identified in the first phase. Simulation results in three categories (energy use, daylighting, and natural ventilation) were analyzed and compared between t he two buildings. In the th ird phase of the research, 16

PAGE 17

the most appropriate BEM tool was selected among the three used in phase two. Guidelines for selecting and using BEM tools were then developed based on the research findings. 17

PAGE 18

CHAPTER 2 LITERATURE REVIEW 2.1 Overview Green building has become a standard practice in the construction industry in the past 10 years. Aside from mora l obligations to integrate energy efficiency into building design and construction, numerous pieces of legislation at the f ederal, state, and local levels have been passed in recent years provid ing either further incentives or mandates to build green. Despite the tr ansition of green building from fad to standard, it is still difficult to predict whether or not a building as designed wil l perform at its desired level upon completion. These uncertainties in re gards to buildings performing at their expected levels and the failures of many projects to meet these performance requirements has led to many building owners forfeiting expected tax credits related to green building. Lawsuits related to build ings failing to meet green performance requirements have become common enough that these types of lawsuits have been coined LEED-igation (Anderson et al. 2010). To aid in the accuracy and predictability of green building per formance, building energy modeling (BEM) tools have been devel oped to simulate the environmental consequences of building design. These tool s aid design professionals in delivering environmentally friendly buildings and provid e greater insurance that buildings will perform at their intended le vels (Azhar & Brown 2009). With green building becoming more of a standard practice in construction, the integration of BEM tools into the design process becomes cr ucial. By allowing design professionals to estimate and simulate bui lding performance in a virtual environment, BEM tools provide feedback related to environmental responsiveness throughout the 18

PAGE 19

design process. The integration of BEM to ols into design not only provides greater certainty to designers and owners of a building s predicted performance, but also aids in the design and construction of increasingly greener buildings (Krygiel and Nies 2008). Building energy modeling c an be applied in many phases of a building lifecycle. While recent research suggests that the mo st important decisions related to building sustainability occur during early design stages (Azhar and Brown, 2009), the potential applications of BEM in facilities m anagement (occupancy and operation phases) are also being explored and implemented. BEM capabilities in terms of input and output ranges are diverse as well. As a research method, the literature re view served not only as a basis for the research but also as a means to develop the crit eria to evaluate these tools. As such it is comprised of two primary sections: BEM applications, and BEM c apabilities. The BEM Applications section investigates the use of BEM in various phas es of the building lifecycle and integration of BEM into various workflows. The BEM Capabil ities section investigates the range of i nputs and outputs in existing BEM tools, and provides an overview of 12 major BEM tools. The lit erature review concludes with a section devoted to the limitations and future development of building energy modeling. 2.2 BEM Applications BEM has proven useful duri ng many phases of the building lifecycle. During the pre-construction phase, BEM is used as an analysis tool to help inform green-minded designers to devise greener design solutions. During the construction phase, BEM aids contractors in acquiring building materi als and components that meet performance requirements. BEM integration into facilit ies management during the building operation phase has also demonstrated positive results by testing potential system adjustments to 19

PAGE 20

increase energy efficiency of existing buildings In this way, BEM can be integrated into both facilities management and renovation and re trofit projects (US General Services Administration 2009). BEM tools are applied to t he design and construction process of green buildings as a design tool, and as a measurement tool. As a design tool, BEM may be integrated into the early design phases when massing, or ientation, and geometry are still being developed. The performance of various conc eptual models may be tested and adjusted based on the feedback provided by BEM simula tions. In an iterative design process, building designers can rely on BEM to inform the development of building form towards greener iterations (K rygiel & Nies 2008). This type of BEM applicati on is perhaps most efficiently employed when BEM is used in conjunction with building information m odeling (BIM) in which a central building model is used throughout the design process. A building information model contains numerous pieces of information related to building design and construction (e.g. geometry, material properties, cost, etc.). As changes are made to the information model, the environmental consequences can be tested in a BEM tool in a relatively seamless way (Schlueter & Thesseling 2009). At the other end of the design process when the building form is finalized and designers are selecting materials and syst ems, BEM tools may be applied in more detail-oriented ways related to design specific ations. During later design stages or even during building occupancy and facilities management, a BEM tool may be applied to more accurately measure various loads, and to aid in adjusting design specifications 20

PAGE 21

(e.g. measuring the thermal performances of two different types of windows, and the projected annual cost savings) (US Gener al Services Administration 2009). 2.2.1 ASHRAE Standard 90.1 The implementation of BEM is outlined through the methodology in ASHRAE Standard 90.1. This BEM process involves developing a benchmark model that uses specified input values for certain building types and climate regions of the United States. More energy efficient iterations of the model are then developed and compared against the benchmark model to dete rmine percent energy savings. This standard is the basis for many green building assessment system s (e.g. LEED and Green Globes) that include possible points towards certificati on related to building energy modeling and energy simulation. ASHRAE 90.1 serves to provide industry standards for various building types in various climatic regi ons to generate benc hmark energy models (ASHRAE 2011). These standards provide the energy model with baseline inputs in regards to occupancy schedules, lighting power densit ies and equipment power densities. The benchmark model is used as the control to test various other design iterations against. In this way, the success of a building design is measured as the percent of energy savings against the benchmark model. For example, the LEED rating system uses this methodology to assess optimization of ener gy performance for LEED Energy and Atmosphere Credit 1 (EA Credit 1). An energy model that demonstrates that the building will save 12% more energy t han the baseline model is able gain one (1) LEED point. The LEED EA credit 1 can provide up to 19 point s if the energy model demonstrates 48% or more energy savings (US Gr een Building Council 2011). 21

PAGE 22

More recent versions of the ASHRAE 90.1 standards for basel ine energy models are setting the bar at higher le vels of energy efficiency maki ng it difficult for designers and energy modelers to develop designs that significantly outperform the baseline model. This is indicative of the trend of sustainable development to set higher standards for energy efficiency. With the bar for energy standards being set ever higher, the integration of environment al analysis during the design process becomes more necessary (ASHRAE 2007, 2010). 2.2.2 Use of BEM in Conceptual Design Phase During the conceptual design phase BEM is integrated into making design decisions related to massing, site selecti on and location, orientation, fenestration strategies, and envelope using simplified and iterative building models (US General Services Administration 2009). In this way, BEM can be used to quickly assess largescale ramifications of various designs, and compare these iterations in various performance parameters. BEM informs building massing by provid ing feedback related to solar exposure and prevailing winds exposure. Similarly, site selection, location of the building within the site, and building orient ation can also be informed by similar environmental conditions. Based on local climate conditions, BEM can be used for testing numerous building envel ope constructions to try to minimize reliance on active heating and cooling systems as well. Sim ilarly, BEM may also be used to make preliminary decisions about building systems during the conceptual design phase. 2.2.3 Use of BEM in Design Development Phase During the design development phase, BEM aids in fi ne tuning decisions on systems selections, building env elope, and glazing strategies. At this stage, the benefits of BIM-based energy analysis become more ev ident. With geometry, site location, and 22

PAGE 23

orientation already established during t he conceptual design phase, building energy modelers may begin to work directly off of mo re detailed BIM model design iterations (as opposed to re-creating the buildng geometry for every design iter ation within the BEM platform). Building energy modelers can isol ate a number of variables to evaluate and compare in more detailed analyse s. Such variables may include glazing type (e.g. low-e, double glazed), visible transmittance of glass, glazing U-value, envelope constructions (with more detailed inputs for envelope la yers and thermal properties), mechanical equipment, and building controls. For exam ple, energy models can compare the daylighting benefits, energy savi ngs, initial cost and lifecycl e costs for two different models of windows based on manufacturer specifications. As decisions become finalized, BEM may also be used to estimate the actual energy performance of the building upon completion (US General Services Administration 2009). 2.2.4 Use of BEM in C onstruction Documents Phase During the construction documents phas e, designers use BEM to finalize estimations of building energy usage (US Ge neral Services Administration, 2009). These estimations may be used to demonstr ate the designs code compliance and ability to save certain levels of energy in relation to a baseline model (as outlined by the methodology described by ASHRAE 90.1) in or der to obtain sustai nability assessment credits (e.g. LEED EA Credit 1) (US Green Bu ilding Council 2007). With BEM aiding in the selection of system manufacturers and suppliers, BEM material and building component databases are also helpful to develop schedules and performance requirements. 23

PAGE 24

2.2.5 Use of BEM in Cons truction and Contracting Phase For contractors, BEM is es pecially useful for projects that must meet certain performance requirements. During the constr uction phase, BEM is used to assess the environmental impacts of change orders and to evaluate and compare the performances of different components when selecting manufa cturers, subcontract ors, and material suppliers (US General Services Administration 2009). For example, a performance requirement may demand that the project obtain LEED indoor environmental quality (IEQ) Credit 8. 1. This credit is obt ained if the project is able to provide adequate dayli ght to at least 75% of regul arly occupied spaces. This may be obtained through the demons tration of computer simu lation and a contractor may quickly test the models of various windo w manufacturers to estimate whether the system will meet IEQ 8.1 requirements. BEM is also useful for contractors in material documentation during the construction phase (Azhar et al. 2011). Materi al documentation is a necessity to obtain up to 12 LEED credit points related to reusabl e / recyclable material selection (Materials and Resources Credits), and non-toxic material s (Indoor Environmental Quality Credits). A recent case study by Azhar et al. (2011) demonstrated how BEM became useful by integrating into a Revit-based BIM wo rkflow for the purposes of material documentation. The study expo rted the BIM model from Revit as a gbXML file and imported it into the BEM so ftware IES. The report used the material takeoffs generated in Revit to provide outputs of reports comparing the model with the requirements for LEED credits. 24

PAGE 25

2.2.6 Use of BEM in F acilities Management Phase The potential of BEM impl ementation into the fac ilities management and operation phases of the building life cycle are still being explored. The General Services Administration (2009) describes one applicati on of BEM in which the energy model is calibrated with metered data from actual build ing operation. System levels can then be adjusted in the virtual environment to identif y errors in system operation and methods to optimize system performance. A similar approach may be taken to retrofit analysis in which a benchmark model is ca librated to simulate existing energy consumption, while iterative energy model s are tested to identify meas ures that can improve energy efficiency. The integration of BEM into a real-time data feed is the ne xt step for sustainability in facilities management. This has been demonstrated in an experiment by Clarke et al. (2002) in which the BEM simulations prov ided real time adjustments based on a live stream of measured data from the actual building. In th is way, building systems may continuously be optimized based on how the buildi ng is being used over time. Platt et al. (2010) also demonstrated fac ilities managers can proactively optimize building energy consumption with the aid of energy modeling. Like the Clarke et al.s study, Platt et al. used a real-time data feed from measured data from actual building operation. Based on these inputs, an energy m odel was developed and calibrated with actual building performance. The energy model integrated a genetic algorithm to optimize system levels and reduce energy consumption. 2.2.7 Integrating BEM with BIM One of the major benefits of using BIM as opposed to traditional design methodologies, is that BIM allo ws for a team of experts from various fields to collaborate 25

PAGE 26

throughout the building lifecycle (Figure 21). In traditional design and delivery methodologies, the work performed on the bu ilding design by architects, structural engineers, MEP engineers, and contractors occurred in rela tive isolation to one another. BIM allows for all of these fields to work collaboratively around a centralized building information model. This is largely due to BIM being more than just 3D graphical representations of a building design. BIM elements have the capacity to hold an array of information related and useful to professionals from diverse areas of expertise. In this way, BIM supports an int egrative and collaborative approach to building design and delivery (Eastman et al. 2008). Interoperabi lity between BIM and other performance analysis software such as many of the BEM software included in this study is also improving to further support and stream line this collaborative environment. A B Figure 2-1. Information exchange in bui lding design and delivery workflows. A) Traditional design/delivery. B) BIM-bas ed collaboration. (Source: original). BEM is a subset of BIM. In typical BIMbased work flows, energy modelers are part of a larger BIM team along wit h specialists in the structur al, MEP, architectural, and construction professions. The interoperability of BEM with BIM platforms like Revit is advantageous in that it allows building designers to test design decisions made within 26

PAGE 27

the BIM platform without having to recreate these changes in the BEM software. The interoperability between BIM and BEM is still developing as errors in the translation process are not uncommon (Schlueter & Thesseling 2009). Some BEM tools also operate as plugins to existing BIM platforms such as Revit or ArchiCAD or 3D modeling software like SketchUp. In this way, design decisions made within the BIM platform can occur with nearly seamless environmental feedback from the energy model. The two primary data schemas that allow BEM software to interoperate with other BIM platforms are Green Builidng Ext ensible Markup Language (gbXML) and Industry Foundation Classes (IFC) (Dong, et. al 2007) GbXML was developed to facilitate interoperability between BIM platforms like Revit and energy analysis software (BEM). GbXML allows objects created in the BIM pl atform to contain information pertinent to green building performance such as thermal conductance, reflectivity, etc. This allows for a streamlined exchange of information be tween 3D BIM modeling and performance analysis (Dong et. al 2007). The IFC data schema was developed by the Interanational Alliance for Interoperability (IAI) in an effort to est ablish a standard and comprehensive data schema for virtual environment architecture, engi neering, and construction (AEC) industry objects (e.g. doors, windows, walls, etc. ). Rather than just being 3D graphical representations of these objects, IFC objects are smart objects with various pieces of information attached to them including materi al properties (Vanlande et al. 2008). IFC information is object-based as opposed to g eometry-based. Geometery is one of many pieces of information attached to objects. In developing IFC, the IAI sought to create a 27

PAGE 28

non-proprietary data schema t hat could be a common file among the various trades in the AEC industry. IFC can also be used during facilities management to facilitate building operation (Khemlani 2007). 2.3 BEM Capabilities As of 2011, the U.S. Depar tment of Energy lists 374 building energy modeling programs in its Building Energy Software T ools Directory (U.S. Department of Energy 2011). The range of capabilities between various existing BEM software is diverse. Typical BEM software operate by entering a set of input s that are run through a simulation engine (Figure 2-2). The simu lation engine provides a range of outputs pertaining to building performance. Different BEM tools come with differ ent arrays of inputs and outputs. Some software may have a narrow range of outputs and a deep set of requi red inputs. Such software focuses on one (or a few) primary area(s) of building performance. Other software may only require a small set of i nputs to generate a wide r ange of outputs. Still, other BEM tools exist that are comprehensive in both input s and outputs (Krygiel & Nies 2008). Figure 2-2. Example of BEM data flow (adapted from US General Services Administration 2009) 28

PAGE 29

Whole building energy usage is affected by numerous factors. In theory, more inputs and factors entered into the building e nergy model will increase the accuracy of simulations. The following sections describe se veral of the typical inputs and outputs in building energy modeling. 2.3.1 Inputs Building Geometry: Building geometry refers to the form, dimensions and orientation of a building. Incl uded in the building geometry is the layout of rooms. This input may also include information on open ings (i.e. windows and doors) and their locations (Krygiel & Nies 2008). Building Location: Building location refers to t he site of the building. The specificity of building location differs between BEM tools. Th is may be input into a BEM tool either as an exact address, global coordinates, zip code, city, or closest city to the site of the building. This may even include an input for local terrain conditions such as urban, forested, rural, etc. This input may sometimes be synonymous with climate and weather data when BEM tools derive these inputs automatically based on the building location (US Department of Energy 2011). Envelope Constructions: Building envelope refers to wa lls, floors, and roof; i.e. the building components that enclose space. The envelope construction input allows users to specify materials and material pr operties for these building components. This input plays a significant role in building thermal performance. Envelope constructions should allow the user to s pecify thermal properties like R-value or U factor, and reflectivity. This allows users to test di fferent materials and si mulate the potential benefits to thermal efficiency (Sozer 2010). 29

PAGE 30

Occupancy Schedule: The occupancy schedule is de rived from the expected number of people inhabiting a building or room, and occupant s presence throughout the day, week, and year. Different thermal z ones may be assigned individual occupancy schedules. These values are typically input into the BEM tool as percentages of the maximum occupancy load per zone. While this type of schedul e is fixed, the development of dynamic schedules has aided the integration of BEM t ools into real-time analysis for facilities management (Kwow & Lee 2009). Operational Schedule: Operational schedules input the times at which building systems are being used and to what capacity (typically by percentage). Operational schedules may be assigned to such building systems as HVAC, lighting, and equipment (IES 2011). HVAC data: HVAC data includes the type of HVAC system intended to be used in the building, its efficiency, des ign fan flows, heating capaci ties, cooling capacities, and exhaust. This may also include estimat ed peak and off-peak times (Clark 2001). Required Indoor Temperature: Required indoor temperat ure is the temperature range to be maintained throughout the year, and is also referred to as thermal comfort range. This may be expressed as heating an d cooling set points, and further described by a throttling range (the temperature thresh old at which the HVAC is triggered on to maintain the intended temperature). ASHRA E 90.1 sets standard thermal ranges that must be maintained for occupant thermal comf ort. Based on this input, some BEM tools will provide outputs on how many hours thr oughout the year the building and HVAC system is not able to meet t he thermal comfort range. Thes e are known as unmet hours. 30

PAGE 31

Unmet hours help BEM users ident ify times of the year when HVAC system levels must be adjusted to meet thermal comfort r equirements (ASHRAE 2011). Weather Data: Weather data files expre ss the climate of the bui lding location (average temperatures, solar exposures, etc. throughout the year). This is often downloaded from a weather file database such as that provided by the US Department of Energy. 2.3.2 Outputs Energy Usage: Energy usage is a calculation of energy used by a building at specific time intervals hourly, daily, monthly, and annually. Common units for energy usage are watts, kilowatts, and kilowatt hours. Energy usage outputs may also included an energy use breakdown showing what perc entage of overall energy was used for different functions, e.g. spac e heating, space cooling, lighting, equipment, pumps, and fans (US Department of Energy 2011). Carbon Emissions Calculations: Carbon emissions calculations allow users to estimate the carbon footprint of the build ing, or how much carbon dioxide (CO2) a building will emit over a spec ified period. This type of ca lculation is based on the amount of energy consumed by a building and what type of energy it is consuming (often assumed based on the buildings geographic loca tion and typical energy sources for that region). The carbon emissions calculation is measured by millions of metric tons (MMT) of CO2 equivalent per kilowatt hour (US Department of Energy 2011). Resource Management: In regards to building energy modeling, resource management refers to an estimation of the available potentials for solar and wind energy. Some tools allow users to create mate rials databases related to the types of materials for construction, and allow user s to estimate land use and energy impacts related to material extraction and manufacturing (Azhar 2011). 31

PAGE 32

Thermal Analysis: Thermal analysis is derived from simulations of heat transfer processes (i.e. convection, conduction, radi ation) through the building and the building envelope. Thermal analysis includes temperat ure profiles and comfort studies of thermal zones (US Department of Energy 2011). Heating / Cooling Load Calculations: Heating and cooling load calculations are the amount of heat or heat remo val over a given time to keep a building at a certain temperature. ASHRAE and the Chartered Institution of Bu ilding Services Engineers (CIBSE) calculation methods are the prominent models for heating and cooling loads. Typical units are in mBtu and kWh (Clark 2001). Airflow: Ventilation simulation may come in the form of natural, HVAC, and/or mixed-mode. These simulations use computat ional fluid dynamics (CFD) to assess the airflow in and around buildings and room objects. The common units for airflow simulations are miles per hour (mph) for natu ral ventilation, and c ubic feet per minute (cfm) for HVAC simulations (Hensen 2003). Natural Ventilation: These simulations may be used to assess passive thermal gains from natural ventilation, or to estima te thermal losses due to infiltration (e.g. opening of doors). This may be assessed as a percentage of heating/ cooling hours lost or gained due to natural ventilation, or as a fact or of the amount of ene rgy lost or gained. Some BEM tools allow users to implem ent operable window sche dules that may be devised to simulate the optimiz ed use of natural ventilation. Computational fluid dynamic (CFD) simulations may also be performed in some BEM tools. This is particularly important to estimate average airflow rates through spaces. CFD analysis is useful in microclimate analysis, in which isolated thermal zones may be assessed and designed 32

PAGE 33

in such a way to maximize airflow to regular ly occupied spaces within the zone. Some analysis tools refer to natural ventilation as in filtration. Infiltration is a more general term that refers to the entry of outdoor air into interior spaces. Infiltration can be both beneficial and detrimental to reducing heating and cooling l oads. In temperate months, infiltration can help reduce cooling loads. However, in colder months, infiltration can raise heating loads. Similarly in warmer months infiltration can also raise cooling loads (Hensen 2003). Solar Analysis: Solar path, position, and radiati on for every hour of the year are typical solar analysis parameters. As it affect s building energy mode ling, solar analysis measures the solar radiation on building su rfaces and its effects on heat transfer. Results from solar analysi s may be used to inform designers about shading strategies, arrangement, position, and orientation of photovoltaic arrays, and may be used to estimate potential passive heat ing gains. Solar analysis is al so an essential calculation for other outputs like daylight ing simulation and thermal analysis. Outputs may be visual, graphical, and/or numerical (Reynolds & Stein 2000). Daylighting Assessment: Daylighting assessment provides users with an estimation of how much the building can rely on natural daylighting to illuminate spaces and reduce the need of electrical lighting. Common outputs are daylight factor and daylight autonomy. Daylight factor is the ratio of indoor illuminance to outdoor illuminance at specified times, and at spec ified locations within spaces (Reynolds & Stein 2000). These locations are defined by the placement of sensor points. Typically, sensor points are placed in the middle of the room and at t he height of a working surface (Velds & Christoffersen 2001. Daylight autonom y is the percent of time that a building 33

PAGE 34

can rely on natural daylighti ng to light the spaces (Rey nolds & Stein 2000). Daylight Autonomy calculation is prefe rred because it is less susceptible to inconsistencies in modeling methodology by taking data from va rious times of day throughout the year. Lighting Design: Simulates the energy efficiency and quality of electrical lighting in a building. This type of analysis may also estimate the annual e nergy consumption for lighting as it relates to a corresponding li ghting or occupancy sche dule. Typical outputs may be in units of kilowatt hours (k Wh) (US Department of Energy 2011). Lifecycle Cost: Lifecycle analysis measures build ing cost, and a range of lifecycle costs such as capital, electr icity and fuel costs, annual main tenance, repair costs, and may sometimes take inflation into account (Younker 2003). 2.4 Existing BEM Tools There are several building energy modeling tools availa ble supporting a wide range of learning curves and capabilities. A survey conducted by Attia et al. (2009) found the top 10 BEM tools by use in the United States. These pr ograms were EnergyPlus, EnergyPlus SketchUp Plugin, eQuest, Autodesk Ecotect, Autodesk Green Building Studio, IES, Visual DOE4 .0, Design Builder, Energy10, and HEED. Of the 10 programs lis ted, the survey found Ecotec t and eQuest to be the most widely used. The followin g sections outline these 10 programs along with three other major BEM tools: Bentley Hevacomp Simulator, Bentley Tas Simulator, and Graphisoft EcoDesigner. 2.4.1 EnergyPlus EnergyPlus is a module-bas ed program that specializes in energy analysis and thermal load calculation. While a number of graphical interfac es are available to be used in conjunction with EnergyPlus, as a st andalone program its inputs and outputs are 34

PAGE 35

entirely text-based. Some of its notable ca pabilities include sub-hourly, userdefined time steps for analysis and thermal load calc ulations that take transient, radiant, conductive, and convective heat transfer, as we ll as moisture absorption/desorption into account. Based on these conditions, EnergyPlu s is able to accurately predict space temperatures and the necessary heating, coo ling, and electrical systems response to maintain occupant comfort (Crawley et al 2005). Some of EnergyPlus other key capabilities include advanced fenestration calculat ions that support variables of shading devices, electrochromatic glazing, and num ber of other high performance commercially available window types; advanced daylighting simulations that provide insight on both interior illuminance levels and heat gains from artificial lighting; and atmospheric pollution calculations providing estimates on CO2, SOx, NOx, CO, particulate matter, and hydrocarbon production from building and energy conversion activities both on and off site (US Department of Energy). Features / Capabilities of EnergyPlus are: Sub-hourly, user-defined time steps Atmospheric pollution calculations Transient heat transfer (conduction, convection, radiation) calculations included in thermal loads calculations Advanced glazing inputs e.g. controllable window blinds, and electrochromic glazing Extensive material and component library including several commercially available products Sketchup Plugin Advantages of EnergyPlus are: Rigorous and in-depth calculations Widely used energy analysis software 35

PAGE 36

Common calculation engine for other BEM tools Free to download Disadvantages of EnergyPlus are: Inputs and outputs are entirely text -based (no graphical interface) Not very user-friendly Limited range of outputs (Smith et al., 2011). 2.4.2 eQuest Developed by the Department of Energy (DOE), eQue st (the Quick Energy Simulation Tool) is a free and comprehensive building energy simulation program. It includes a graphical interfac e and building creation wizard to guide users through the basic building inputs. The energy efficien cy measure (EEM) wizard allows user to include more detailed and performance-based i nputs to compare the results of various design alternatives (US Department of E nergy, 2011). It uses the latest DOE-2 simulation engine and provides extensive and detailed result s in its simulation reports that can be compared side by side with simulations using different combinations of energy efficiency measures. The report is br oken down into hourly time steps over the entire year (Crawley et al. 2005). Features / Capabilitie s of eQuest are: Uses DOE 2.2 building energy analysis software as its calculation engine Wizard-based inputs Detailed analysis reporting broken down by hourly time-steps and on a zonal basis Advantages of eQuest are: Supports simple to detailed models Quick calculation time Validated by US Departm ent of Energy and ASHRAE Provides a wide range of outputs Free to download 36

PAGE 37

Disadvantages of eQuest are: Limited and simplified infiltrati on / natural ventilation simulations 3-D model geometry is built in the software (can not be imported) Not very user-friendly outside of wizard-based inputs Does not simulate interior gl azing in daylighting calculations Sensitive to model errors 2.4.3 Autodesk Ecotect Ecotect is a comprehensive energy anal ysis software with a focus on graphical output. Analyses types supported by Ecotect include (but are not lim ited to) thermal, solar, lighting analysis and acoustic analysis (US Department of Energy). Ecotects most notable feature is its robust and inte ractive graphical outputs. Each analysis type can be represented in a num ber of different graphs or with a versatile analysis grid that can be mapped over any surfac e of the model. Ecotects graphical outputs may be saved and exported as bitmaps, metafiles, and in some cases as animations. Ecotect is intended to be an early design phase t ool. Ecotects developer, Autodesk argues that the most critical and effective decisions pertaini ng to green design are made in the conceptual design phase. Ecotect is tailored to this idea by being able to provide visual and analytic feedback to extremely simple sketch models (Crawley et al. 2005). Features / Capabilitie s of Ecotect are: IFC and gbXML import Analysis grid Dynamic graphical outputs animations Solar, thermal, light ing and acoustics analysis Lifecycle analysis Advantages of Ecotect are: Building geometry editing Scalable graphical analyses 37

PAGE 38

Online Autodesk user support (AUGI Forums) Disadvantages of Ecotect are: long calcuation times sensitivity to modeling errors user interface is not user friendly (Azhar, 2009) 2.4.4 Autodesk Green Building Studio Green Building Studio is a web-based BEM tool. As such it does not include its own 3D modeler and must rely on a gbXML-enabled or IFC-enabled BIM or 3D modeling platform for the creation of bu ilding geometry. Upon importing building geometry, Green Building Stud io guides the user through a baseline simulation providing a report detailing estimated CO2 emissions, ener gy analysis, potential for natural ventilation, lifecycle cost and other analyses. Alternative simulations using various combinations of energy efficiency measures may then be run and compared to the baseline and each other (U S Department of Energy, 2011) Another aspect of Green Building Studio is that as a web-based energy analysis program, simulations are run through the internet as opposed to the users microcomputer. This allows for simulations to be performed much quicker than with most other computer-powered simulation programs (Azhar, 2009). Features / Capabilities of Green Building Studio are: Energy usage, carbon emissi ons, daylighting, ventilation Lifecycle assessment Online interface Alternative run comparisons Advantages of Green Buil ding Studio are: Interoperability with Revit Fast calculation times Requires minimal preparation to run the base simulation 38

PAGE 39

Disadvantages of Green Building Studio are: Unable to customize outputs Relies on third party softwar e to model building geometry 2.4.5 Graphisoft EcoDesigner EcoDesigner allows for immediate feedback pertaining to environmental performance during early design stages. It is a t ool that is integrated into the Graphisoft ArchiCAD BIM platform. As such it a llows energy analysis to be performed very quickly while designing in ArchiCAD. In addition to building geom etry in ArchiCAD, EcoDesigner provides inputs for HVAC, lo cation, and thermal pr operties of building envelope elements (Thoo, 2008). Features / Capabilities of EcoDesigner are: Strusoft Corecalculation engine (ASHRAE 90.1 compliant) ArchicCAD plugin Calculates energy consumption, car bon footprint, monthly energy breakdown Advantages of EcoDesigner are: Interoperability as a plugin to ArchiCAD User-friendly interface Quick calculation types Disadvantages of Ec oDesigner are: Provides minimal opport unity for customization Relies on default values for many calculations Limited options for inputs and outputs 2.4.6 IES (IES ) IES is a comprehensive BEM software that uses a set of modules to perform various calculations and simulations. T hese modules are all linked together by a common user interface and a single integrated data model. Modules included in the IES 39

PAGE 40

package include ModelIT for building geometry creation, ApacheCalc for load analysis, ApacheSim for thermal analysis, Ma croFlo for natural ventilation analysis, ApacheHVAC (HVAC simulation using component s), SunCast for shading visualization, MicroFlo for three-dimensional comput ational fluid dynamic calculations, FlucsPro/Radiance for daylighting analysis, DE FT for model optimization, LifeCycle for life-cycle cost and energy analysis, and Simule x for building egress simulations (Crawley et al. 2008). Features / Capabilitie s of IES are: Outputs include energy usage, carbon emissions, thermal analysis, ventilation and airflow, solar analysis, day lighting, lifecycle analysis Building geometry editing and modeling Analysis grid gbXML model error check Advantages of IES are: Comprehensive building performance tool User-friendly interface Includes direct plugin to Revit to improve interoperability Disadvantages of IES are: Analysis results are saved in different folders gbXML model error check is required (Azhar, 2009) 2.4.7 Bentley Hevacomp Simulator Hevacomp Simulator uses EnergyPlus as its simulation engine. As such it creates a connection between BIM platforms lik e Bentley and Revit and uses those as a graphical interface for Ener gyPlus analyses. Hevacomp also provides compliance 40

PAGE 41

services to support UK Part L and ASHRAE 90. 1 compliant buildings (Bentley Systems, 2011). Features / Capabilities of Hevacomp Simulator are: Building energy standard compliance tools for CIBSE, ASHRAE, ISO, and LEED EnergyPlus calculation engine Calculations include energy usage, natur al and mechanical ventilation, airflow, thermal analysis, and renewable ener gy potential (solar and wind) gbXML enabled Advantages of Hevacomp Simulator are: Interoperability with other Bentley-based BIM software Compliance with building energy standards and certification Detailed and accurate analysis Predefined and user-defined HVAC systems Disadvantages of Hevacomp Simulator are: Outputs are limited to energy and thermal analysis Requires some expertise in MEP Limited user support 2.4.8 Bentley Tas Simulator Tass primary function is thermal analysis Thermal simulations provide the basis for other analyses including energy consumpti on, energy operating cost s, lifecycle costs, CO2 emissions, and occupant comf ort. Tas also provides features allowing for the simulation of passive design strategies like natural ventilation. An other feature included in Tas is a compliance check that allows the user to ensure that the design is compliant with major green standards like ASHRAE 90.1 LEED credit, UK regulations Part L2 and EP certification (Bentley Systems, 2011). Features / Capabilities of Tas Simulator are: 41

PAGE 42

gbXML import Outputs include thermal analysis, natural ventilation and airflo w, energy use, CO2 emissions, occupancy comfor t, and component sizing Compliance with ASHRAE 90.1, LEED, and CIBSE Advantages of Tas Simulator are: Positive feedback from users on gbXML import Provides feedback for component sizing Includes a Facilities Management Tool to model changes to systems while in operation Disadvantages of Tas Simulator are: Tailored towards detailed analysis Requires some MEP expertise Limited user support 2.4.9 DesignBuilder DesignBuilder was developed to be an ea sy-to-use BEM software. It is best suited for early design stage m odeling in which the user can quickly evaluate various design options for energy consumption and environmental comfort with the option of including detailed analysis for potential natur al ventilation (US Department of Energy, 2011). Features / Capabilities of DesignBuilder are: Outputs include energy us age, CO2 emissions, solar shading, daylighting, natural ventilation and airflow, and thermal analysis Calculates heat transmission (conduc tion, convection, radiation). EnergyPlus calculation engine Advantages of Desi gnBuilder are: Building geometry can be altered Natural ventilation simulations require minimal preparation work 42

PAGE 43

User-friendly Disadvantages of DesignBuilder are: Limited HVAC inputs Limited interoperability with 3D/BIM platforms 2.4.10 Energy10 The major strength of Energy 10 is its automatic output of more-efficient design alternatives based on the initia l baseline simulation. Design al ternatives use a number of predefined strategies alteri ng building envelope and buildi ng systems (HVAC, lighting, daylighting, and photovoltaic potential). A lim itation of Energy10 is that it only analyzes one or two thermal zones at a time. As such it is better suited for the analysis of smaller projects (10,000 square feet or less). Energy10 also includes a lifecycle cost analysis tool (Crawley et al. 2008). Features / Capabilitie s of Energy10 are: Energy, thermal, and daylighting simulations Hourly time-steps for calculations over entire year Comparison of alternative designs ASHRAELIB ASHRAE compliant building components library Advantages of Energy10 are: Requires minimal inputs to run baseline simulation Calculation speed is fast Default values are adjustable Disadvantages of Energy10 are: Limited to building models of one or two thermal zones, and floor area under 10,000 SF. Limited HVAC inputs Requires some programming knowledge 43

PAGE 44

2.4.11 HEED HEED is a free, user-friendly, singl e zone energy simulation program developed by UCLA. Its interface is largely wizard based, and 3d modeler is exceptionally easy to use. Relying only on floor area, number of stories, locati on, and building type as inputs, HEED generates two design it erations with one being 30% more energy efficient than the other. HEED can manage up to 9 iterations for 25 projects. Features / Capabilit ies of HEED are: Passive design inputs thermal mass, night ventilation, high-performance glazing Simulates energy usage, CO 2 emissions, lifecycle cost Advantages of HEED are: User-friendly Provides detailed inputs Automatically generates design alternatives Disadvantages of HEED are: Limited to four thermal zones Limited HVAC options Weather data is lim ited to California 2.4.12 Visual DOE 4.0 Visual DOE4.0 is a windows interface for the DOE2.1 building energy calculation engine. Users create the buildi ng geometry in Visual DOE by importing a DXF file of the floor plan from a CAD so ftware and filling in the spaces using blocks in the model editor. Users can specify bulidng envelope constructions and HVAC system types from the libraries. Visual DOE also features a design alternative generat or that can provide up to 99 different design iteratio ns for building envelope and HVAC. Features / Capabilities of Visual DOE 4.0 are: Thermal and energy analysis 44

PAGE 45

DOE 2.1E calculation engine Design alternative generator Hourly time step results Advantages of Visual DOE 4.0 are: Users can compare several alternatives very quickly Requires minimal inputs to run base simulation Useful as schematic design tool Disadvantages of Visu al DOE 4.0 are: Building geometry must be recreated in the software Limited user support Limited outputs 2.5 Limitations of Building Energy Modeling Although building energy modeling presents designers, builders, and building owners with an array of powerful tools to assess and predict building performance, many of these programs are yet to be validated. It should be not ed that these tools provide only estimates (some much rougher than other s). While the implementation of many inputs allows for accurate models, building energy is affected by many factors that cannot be predicted. Climate data is based on averages for various locations, and differs from year to year. Building occupancy ma y be simulated by an occupancy schedule, however it is impossible to predict the actual behavior of occupants during building operation. The variability in how building occupancy actually occurs is a common source for energy model errors. Because of this variability, accuracy of predicting how a building will perform upon completion is compromised. Building energy simulation is tapping into the potential of integrating real-time data feed into the calibration process, however these developments are still in their early stages. Such technology aids in both incr easing the accuracy of energy modeling, and 45

PAGE 46

improving energy efficiency by using energy si mulation to aid in the optimization of building performance based on actual tendencies in building operation. As a design tool, BEM pushes architec ts and engineers towards an integrated design approach. Interoperability betw een BEM and BIM and other 3D modeling applications is supported by m any programs. However, it is not uncommon for errors in the building model to arise in the transla tion process between BIM to BEM (Azhar & Brown 2009). There is still much potential to push interoperability further to make design and environmental analysis an even mo re seamless process (Thoo 2008). 46

PAGE 47

CHAPTER 3 RESEARCH METHODOLOGY The research methodology was broken down into three parts based on the three objectives. Section 3.1 describes the research methodology to conduct a cross evaluation of 12 major BEM tools. Section 3.2 provides the methodology of the case study; and section 3.3 describes the methodology for the re-evaluation and development of guidelines for using BEM for the environmental analysis of high performance buildings. 3.1 Initial Evaluation Twelve major BEM tools were selected fo r the initial evaluat ion. These programs were Graphisoft EcoDesigner, Bentley Tas Simulator V8i, Bentley Hevacomp Simulator V8i, Autodesk Ecotect, Autodesk Green Building Studio, IES , DesignBuilder, Visual DOE 4.0, Energy10, EnergyPlus, E-Quest and HEED. These BEM tools were selected based on a survey study by Attia et al. (2009) to identify the most widely used energy simulation softwar e in the United States. The research included Bentley Tas Simulator V8i and Bentley Hevacomp Simulator V8i in addition to the top 10 BEM tool s identified in the Attia et al. survey, as these are prevalent BEM tools used in the United Kingdom. The initial evaluation assesses these programs using four main crit eria: interoperability, user-fri endliness, available inputs, and available outputs. Within each criterion were a number of sub-crit eria that were used as a checklist for each criterion. The sub-cr iteria were identified based on the literature review. Previous studies that compared th e capabilities of existi ng BEM tools were synthesized into the sub-criteria checklis ts. These studies included Crawley et al. (2008), Attia et al. (2010) Azhar et al. (2009) and Azhar et al. (201 1), as well as general 47

PAGE 48

information provided for several BEM tools in the US Department of Energys Building Energy Software Tools Directory (2011). The Crawley et al.s study (2008) was particularly instrumental in developing the su b-criteria checklist for available inputs and available outputs. Azhar et al.s study (2009), along with other BIM-oriented studies including Thoos (2008), and Eastman et al.s (2008) were used to develop the subcriteria checklist for interoperability. Azhar et al.s study(2011) and Attia et al.s study (2010) were primary resource s in developing the sub-criteria checklist for userfriendliness. The scoring system placed an ev en weight of 1 point maximum for each criterion. Per criterion, the BEM tool received a sco re between 0 and 1 based on the percentage of sub-criteria s upported by the software. Each BEM tool was scored using this system to determine the top three programs of the 12 used in this portion of the study. 3.2 Case Study The top three BEM programs identified by the initial evaluation were used to conduct a case study comparing the perform ance of two buildings. These buildings (both on the University of Flor ida campus in Gainesville, Florida) were Rinker Hall (a LEED gold certified buildin g) and Gerson Hall (a non-LEED certified building). BIM models were prepared for each building using Revit Architecture 2012. Each model was exported as a gbXML file from Revit (Figure 3-1) and imported into each of the three programs. The models were exported as simple with shading selected in the gbXML export window in Revit to specify the le vel of detail. 48

PAGE 49

Figure 3-1. GbXML files of buildings used in the case study ex ported from Revit Architecture. A) Rinker Hall. B) Gerson Hall Specifications pertinent to each building (Tab le 3-1) were input into each BEM tool (or to the best of the softw ares capability). Each BEM progr am was used to simulate both buildings performance in energy usage, daylighting, and natural ventilation. The ability to input these specifications was di fferent between BEM tools. Some software like Green Building Studio, allow for building constructions to be selected from a dropdown menu, but do not provi de the user with the ability to specify construction layers and respective thermal properties. The case study used energy use intensity (k Btu/SF) to compare the two buildings energy performance. Energy use intensity (EUI) was derived from the overall annual energy usage divided by the bui ldings floor area. EUI was used as the unit to compare the two buildings performances so as to remove any difference in energy usage based on the difference in the two buildings square footages. A la rger building with a greater area of conditioned space is more likely to consume more energy than a building with a smaller area of conditioned space. 49

PAGE 50

Table 3-1. Comparison of the bu ildings used in the case study Building Characteristics Rinker Hall Gerson Hall Date of completion 2003 2004 Location Gainesville, FL Gainesville, FL Area of conditioned space (sq. ft.) 42,719 38,632 HVAC system Variable Air Volume with Energy Recovery Ventilation Variable Air Volume with Terminal Reheat Building envelope construction (from exterior to interior) metal panel, 5.5 R20 cellulose insulation, 2 rigid insulation, gypsum board 4 brick veneer, 2 air gap / damproofing, 12 CMU, 5/8 GWB on 1-1/2 studs with rigid insulation Exterior wall UValue 0.033 0.097 Glazing type Low-E, double-glazed, insulated Low-E, double-glazed Glazing U-Value 0.53 0.66 Window to Wall Area Ratio 0.22 0.20 Albedo (Roof Reflectance) 0.80 0.41 To compare the daylighting performance, four rooms from each building were selected (Table 3-2). The study compared similar rooms (similarities based on orientation, room area, and r oom function) between the two buildings for each of the three programs using daylight factor as the common unit. I deally the study would have compared daylighting based on day light autonomy, but could not due to lim itations of the software. Because the two bui lding have different orientati ons (the long axis of Rinker Hall is oriented east to west while the long axis of Gerson Hall is oriented north to south), the similarities between glaz ing orientations for room co mparisons were limited. For instance, the rooms selected for comparis on for the office room type and graduate studio room type had inconsiste nt glazing orientations because no such rooms exist in the two buildings that have the same room function and glazing orie ntation. The rooms selected were the closest fits of the rooms available for analysis. 50

PAGE 51

Table 3-2. Profiles of rooms compared for daylighting analysis Rinker Hall Gerson Hall Room Designation Area (sq. ft.) Glazing Orientation Room Designation Area (sq. ft.) Glazing Orientation 303 Main Conference 589 North 327 Large Conference 768 North 322 Faculty Office 139 West 324 Office 146 North 240 Est./Dwg./Sch 1334 East 122 Medium Classroom 1162 East 340 CCE 527 East 329 PhD Office 274 North The case study also assessed the natural ventilation and potential energy savings of the two buildings using each of the three BEM tools. In par ticular, the research sought to estimate the potential energy savings due to reliance on natural ventilation (i.e. operation of operable windows). For natural ventilation analysis, the research assumed optimized use of operable windows for both buildings. This meant that operable windows were open at all moments of the year when outdoor climat ic conditions would benefit energy efficiency by reducing cooling loads. Again, differ ent BEM tools provided for different modeling met hodologies, so the comparison was limited by the types of outputs provided by the three software used in the case study. 3.3 Re-evaluation of BEM Tools Used in the Case Study Upon completing the case study, a re-evalu ation of the top three BEM tools was conducted using a similar set of criteria as the initial cross evaluat ion. Adjustments and additions were applied to the criteria and subcriteria based on information gathered during the case study. The four criteria used in the re-eval uation were interoperability, user-friendliness, versatility (of inputs and outputs), and calculation speed with updated subcriteria for each criterion. 51

PAGE 52

First, the scoring system was used to se lect the best program based on an even weight applied to each of the four criteria. A matr ix was then developed applying different weights to criteria based on order of importance for the potential user. In this way, a potential BEM user c ould use the matrix by firs t identifying the order of importance of the four criter ia, and then be directed to the BEM tool most suitable to their preference. 3.4 Developing Guidelines for BEM Selection and Application Guidelines were organized by assessi ng the applicability of BEM to various building lifecycle phases. These building lif ecycle phases were conceptual design, design development, construction documents construction and contracting, and facilities management. Guidelines were developed based on the use of each BEM program during the case study. During the case study, the energy modeling methodology for each BEM tool was investi gated. The steps in the modeling process under investigation we re the following: model preparation in BI M (Revit Architecture) model preparation in BEM weather data acquisition schedule implementation energy analysis daylighting analysis natural ventilation analysis results analysis A log was maintained for each step doc umenting complications, advantages, disadvantages, observations and the locations of help files / user manuals / tutorial sources that were used for guidance dur ing the energy mode ling process. The spreadsheets for each BEM tool are available in Appendix C. 52

PAGE 53

CHAPTER 4 RESULTS The results related to the four major objec tives of the research are presented in the following sections. The initial evaluati on identified IES, Ecotect, and Green Building Studio to be the most appropriate BEM tools out of t he 12 evaluated. These three BEM tools were used in the case st udy to compare the ener gy usage, daylighting performance, and natural ventila tion potential of Rinker Hall (a LEED goldcertified building), and Gerson Hall (a non-LEED certif ied building). Overall, the results showed that Rinker Hall performed better than Ge rson Hall in regard to energy usage and daylighting performance (for t he rooms selected), while Gerson Hall performed better than Rinker Hall in natural ventilation potential. 4.1 Initial Evaluation The initial evaluation us ed the scoring system illustra ted in Figure 4-1. The comprehensive score for each BEM tool was calculated as the sum of the individual criterion scores. The four criter ia were interoperability, user friendliness, available inputs and available outputs. Each criterion was scor ed as the fraction of subcriteria supported by each BEM tool over the tota l number of subcriteria eval uated for each criterion. The criterion interoperability had five total subcriteria in the checklist. Thus the criterion score for interoperability was calculated as x/5, where x denotes number of subcriteria supported by the BEM tool. Us er friendliness had eight subcriteria and was calculated as x/8. Available inputs had 25 subcriteria and was calculated as x/25, and available outputs had 20 subcriteria and was calculated as x/20. 53

PAGE 54

Figure 4-1. Initial evaluation scoring system with criteria and subcriteria Of the 12 BEM tools investigat ed in the initial evaluati on, Ecotect, Green Building Studio, and IES scored the highest and were selected for use in the case study. The following sections illustrate the breakdown of the initial evaluation based on the four criteria (user friend liness, interoperability, required inputs, and versatility). Each section shows the breakdown of subcriteria that went into each BEM tools score. 4.1.1 User Friendliness The results from the analysis showed that th e most user friendly software of the 12 BEM tools evaluated, were Energy10, Gr een Building Studio, and HEED received the highest scores for user friendliness (Figure 4-2). Each of these BEM tools provided for six out of the eight s ub-criteria included in the User Friendliness checklist. Ecotect, DesignBuilder, Visual DO E4.0 and IES each provided for five out of the eight sub-criteria. EQues t, EcoDesigner, Tas, Hevacomp, and EnergyPlus scored the lowest out of the 12 BEM t ools providing for four out of the eight subcriteria in the Us er Friendliness checklist. 54

PAGE 55

Figure 4-2. User Friendliness Energy10, Green Building Studio, and HEED had the highest scores in the user friendliness evaluation. These softwar e provide users with ex tensive sources of user-help and require minimal expertise to get base run results. One of Energy10s major strengths as a user-friendly BEM tool is its capability to autom atically provide the user with more energy-efficient design alternatives. Users also provide very few inputs in order to run a base simulation. Since Ener gy10 is only intended for one-zone and twozone analysis, the modeling proc ess is extremely simplified which is beneficial for users with limited experience in 3D modeling. Similarly, HE ED relies on very few inputs to generate energy results. Although the program is extremely si mple and intuitive to use, many of its default settings and weather files are tailored to California, which can complicate the modeling process for projects in other climatic regions. Green Building Studio relies on third-party so ftware (like Revit) for the creation of building geometry. When the BIM model is exported as a gbXML file to Green Buildling Studio, the process is not unlike HEED and Energy10 Users fill out a quick questionnaire to specify building type and location before the init ial simulation can run. As Green Building 55

PAGE 56

Studio is an Autodesk product, users also benef it from an extensive set of tutorials and user-forums for user-friendliness. EQuest, EcoDesigner, and EnergyPlus received low scores in this criterion (4 out of 7). These BEM t ools had did not have simple user interfaces, had limited potential for customization, and did not provide feedback related to more environmentally friendly design alternatives. The Bentley BEM tools (Tas and Hevacomp Simulator) also received low scores as these programs are tailored to complex yet specialized ana lyses, and are intended for use by qualified architects and engineers. 4.1.2 Interoperability IES scored highest (five out of five possible points) for interoperability. IES has capability of sharing information with each of the software / file types evaluated. These included interoperabi lity with gbXML file types and Google SketchUp. EcoDesigner, Tas, Gr een Building Studio, and Hevacomp provided interoperability wit h all but SketchUp and sco red four out of five. DesignBuilder and Visual DO E 4.0 allow DXF import to aid in the creation of building geometry, but 3D models must be developed in each pr ograms in-house model builder. HEED and Energy10 demonstrated the lowest degree of interoperability with none of t he programs or file types be ing supported by import or export capability. For these two programs, building geomet ry must be created within the BEM tool. The results for inte roperability evaluation are il lustrated in Figure 4-3. 56

PAGE 57

Figure 4-3. Interoperability 4.1.3 Available Inputs IES had the highest score (20 out of 25) based on the available inputs evaluation, followed by Ecotect (19 out of 25) and eQuest (18 out of 25). HEED (score six out of 25), Energy10 (eight out of 25), Tas and Hevacomp (both scored nine out of 25) had the lowest scores regarding available inputs. The top three BEM tools in available inputs (IES, Ec otect, and eQuest) ma y be considered the more versatile software. Users can input val ues for a wider range of variables into the model. While certain inputs were relatively co nstant for most of the software (building geometry, location, material pr operties), the inputs that set IES, Ecotect, and eQuest apart were more detail oriented. IES for example provides inputs for MEP models with HVAC compone nt sizing, and plant data. IES and Ecotect both have lighting system input s that provide users with the ability to design and simulate the effectiveness of electrical li ghting. IES, Ecotect, and eQuest all have inputs for required internal temperat ure, type of energy used, occupancy and 57

PAGE 58

building function. The results from the availabl e inputs evaluation are illustrated in Figure 4-4. Figure 4-4. Available Inputs 4.1.4 Available Outputs Green Building Studio (score 19 out of 20 possible), Ecotect (18 out of 20), and IES (19 out of 20) had the most outputs of t hose included in the available outputs evaluation. The software that received the lowest scores in this category were EcoDesigner (6 out of 20), Visual DOE4.0 (6 out of 20), and HEED (8 out of 20). The software that received the highest sco res (Green Building Studio, IES, and Ecotect) had a wider rang e of building performance simulations. Some of the 58

PAGE 59

outputs included in all of the top three software that set them apart from the others included tools for lifecycle cost and assessm ent, LEED integration, and wind energy potential. The results of t he available outputs evaluatio n are shown in Figure 4-5. Figure 4-5. Available Outputs 4.1.5 Cumulative Score The cumulative scores used in the BEM t ool evaluation were calculated by first converting the criteria scores into percentages. The final score ( c ) was the sum of these percentages with each criterion receivi ng the same weight. The final score was calculated by equation [4-1]: c = c1+c2+c3+c4 .[4-1] where: c1 = User friendliness; c1 = x/8 c2 = Interoperability ; c2 = x/5, c3 = Available inputs; c3 = x/25 c4 = Availa ble outputs; c4 = x/15, x = num ber of subcriteria supported by BEM tool for the respective criterion 59

PAGE 60

Results of the overall score s are illustrated in Figure 4-6. IES received the highest score in the evaluatio n (3.38 out of 4). Ecotect received the second highest score (3.14 / 4), and Green Building Studio re ceived the third highest (3.06 / 4). These three BEM tools were selected for use in the case study. Figure 4-6. Overall scores of the BEM tool in itial evaluation Figure 4-7 depicts the overall versatility of the BEM tool s in terms of available inputs and available outputs. BEM tools that had high scores in available inputs (12.5 25) and available outputs (10 20) fell in quadrant B. BEM tools with higher scores in available outputs (10 20) and lower scores in available inputs (0 12.5) fell in quadrant A; tools with lower scores in available outputs (0 10) and higher scores in available inputs (12.5 25) fell in quadr ant C; and tools that had low scores in both available inputs (0 12.5) and available out puts (0 10) fell in quadrant D. BEM tools that were in quadrant A (higher scores in available outputs and lower scores in available inputs) included Ener gy10, Tas, Hevacomp, and Visual DOE4.0. BEM tools that we re in quadrant B (higher scores for both available inputs and available outputs) were Green Bu ilding Studio, eQuest, Ecotect, and 60

PAGE 61

IES. EnergyPlus and EcoDesigner fell in quadrant C, which is characterized by limited outputs with a wider range of inputs; and HEED and DesignBuilder fell in quadrant D (low scores in both available inputs and outputs). Figure 4-7. The scores for available i nputs and available output s of the BEM tools 4.2 Case Study The top three BEM software tools (Eco tect, Green Building Studio, and IES) were used in the case study. Simulations of each building were performed by each BEM tool and assessed energy usage, daylighting, and natural ventilation. Overall, the simulations showed that the LEED certifi ed building (Rinker Hall) would perform better than the non-LEED cert ified building (Gerson Hall) in regards to annual energy usage (by both overall energy use and EUI) and da ylighting performance for the selected 61

PAGE 62

rooms. Simulation results showed that Ge rson Hall performed better than Rinker Hall in regard to natural v entilation potential. 4.2.1 Energy Usage Regarding energy usage, Rinker Hall, the LEED-certified building, performed better than Gerson Hall in both total annual energy usage and in energy use intensity (EUI). This was true in all three BEM programs (Figur e 4-8). Ecotect simulations showed that Rinker Hall would consume less energy than Gerson Hall (56% difference between EUIs). Green Building Studio calculati ons also showed that Rinker Hall would consume less energy than Gerson Hall (20% di fference between EUIs). Similarly, IES simulations estimated that Rinker Hall would consume less energy than Gerson Hall (36% difference between EUIs). Figure 4-8. Energy use intensity (EUI) co mparison by building a nd by BEM tool. Dotted line denotes the CBECS nati onal median EUI for educational building types (104 kBtu/SF) As of 2003, the CBECS nat ional median energy use intensity for Education (College/University) building types is estima ted to be 104 kBtu/SF. This serves as a 62

PAGE 63

baseline value to compare t he energy simulations against. T he lower the EUI, the more energy efficient the building is. In all thr ee BEM tools, Rinker Ha ll was simulated to perform better than the national average. Ecotec t simulations estimated an EUI of 45 kBtu/sf; Green Building Studio simulated an EUI of 58 kBtu/sf; and IES simulated an EUI of 61 kBtu/sf. When co mpared against the CBECS national average, simulations of Gerson Hall had mixed results. Green Building Studio estimated that it would perform better with an EUI of 73 kBtu/sf; t he Ecotect simulation estimated that it would perform very close to the nat ional average with 103.18 kBtu/sf; and the IES simulation showed that Gerson Hall would exceed the nat ional mean with an EUI of 126 kBtu/sf. For all three BEM software, the energy use breakdowns for the two buildings showed that the greatest amount of energy was used for space cooling (Figure 4-9). Figure 4-9. Energy use breakdown for two buildings used in case study using three BEM tools. 63

PAGE 64

Ecotect simulations broke down energy use into two categories: space heating and space cooling. For both Rinker Hall and Ge rson Hall, a larger proportion of energy was used for space cooling than for space heating. Green Building Studio broke down energy use based on percentage of energy used for space heating, heat rejection, fans, pumps & auxiliary, space cooling, exterior loads, miscellaneous eq uipment and lights. Again, the largest proportion of energy was used for space cooling for both Rinker Hall and Gerson Hall. Energy use breakdowns obtained by IES simulations were broken down into the categor ies of space heating, fans, pumps & auxiliary, space cooling, miscellaneous equipment, and lighting. Results for Rinker Hall and Gerson Hall showed again showed that the largest proporti on of energy was used of space cooling. 4.2.2 Daylighting Performance The daylighting performance of each bu ilding could be com pared within each program, but results could not be compared between the th ree BEM programs due to the fact that daylight factor wa s not calculated in a consistent manner. Only Ecotect and IES allow the user to specify the placement of se nsor points at which the daylight level is measured. None of the three tools allow the user to specify the date and time at which the daylight factor is calculated. The rooms in Rinker Hall had higher daylight factors than their counterparts in Gerson Hall, but with some exceptions (Table 4-1). Within each BEM tool, Rinker Halls conference room, classroom, and graduate st udent office suite performed better than those in Gerson Hall. The faculty office had mixed results. Ecotect and Green Building Studio predicted higher daylight factors for the office in Gerson Hall, while IES estimated the faculty office in Rinker Hall to perform better. Overall Rinker Hall appeared to have better daylighting performance than Gerson Hall based on the rooms simulated 64

PAGE 65

in the study. This may be attributed to Rinke r Hall having a higher window to wall ratio (see Table 3-1). Table 4-1. Comparison of daylight factors for the selected rooms. Room Function Buildin g Ecotect Green Building Studio IES Conference Rinker Hall Gerson 11.48% 6.30% 13.70% Room Hall 3.37% 0.70% 4.80% Rinker Hall 2.74% 0.30% 6.40% Gerson Facult y Office Hall 3.22% 1.00% 5.00% Rinker Hall 3.98% 0.80% 3.80% Gerson Classroom Hall 3.00% 0.20% 1.10% Rinker Hall 3.89% 0.90% 2.60% Gerson Graduate studio Hall 1.79% 0.50% 3.10% Highlighted values are great er than the minimum required daylight factor (2%) for adequate daylighting. 4.2.3 Natural Ventilation Each of the three BEM software tools assessed natural ventilation in different ways (Table 4-2). Green Building Studio provided outputs related to the amount of energy that could be saved through the use of natural ventilation. Na tural ventilation potential in Ecotect was obtained by running two simulations one with operable windows activated (allowing for natural ventilation at optimal time s of the year) and one without operable windows activated. IES simula ted natural ventilation in terms of average airflow (CFM) per square foot. Green Building Studio simulations showed that Gerson Hall (potential annual energy savings of 57,883 kWh) could possibl y save more energy (44% difference) through natural ventilation than Rinker Hall ( potential annual energy savings of 32,254 kWh). Potential energy savings from natural ventilation were calculated in Ecotect by subtracting the overall energy use of the mo dels with natural ventilation activated from 65

PAGE 66

energy use values of the benchmark models. Ecotect simulations also showed that Gerson Hall (potential savings of 142,043 kWh) could possibly save more energy (35% difference) than Rinker Hall (potential savi ngs of 92,516 kWh). IES was able to assess natural ventilation by providing av erage annual infiltration rates (cfm) for each zone. Gerson Hall had an average natural ventilation rate of 0.033 CFM per square foot averaged over the entire inhabi table building floor area compared to Rinkers average natural ventilation rate of 0.022 CFM per square foot. Thus Gerson Hall seemed to provide a 33% higher ventilaton rate than Rinker Hall. Table 4-2. Natural Ventilation Simu lation Results for three BEM tools. Potential energy savings from natural ventilation (kWh) Rinker Hall 92,516 Ecotect Gerson Hall 142,043 Potential energy savings fr om natural ventilation (kWh) Rinker Hall 32,254 Green Building Studio Gerson Hall 57,883 Average CFM per square foot from natural ventilation Rinker Hall 0.022 IES Gerson Hall 0.033 The probable reason why Gerson Hall out performed Rinker Hall based on natural ventilation results obtained by each of the three BEM tools, is Gerson Halls orientation towards prevailing winds. Whereas Rinker Hall is oriented longitudina lly north to south, Gerson Hall is oriented eas t to west (Figure 4-10). Prevailing winds in the summer months for these building locations come from the south-southwest. By exposing a larger surf ace area of the buildin g to the prevailing winds (by orienting itself east to west), Gerson Hall has more interior rooms exposed to prevailing wind-assisted natural ventilation for times of year when natural ventilation is beneficial to reducing the cooling load. 66

PAGE 67

Figure 4-10. Diagram of build ing orientations relative to summertime prevailing winds. 4.3 Re-Evaluation of Building Energy Modeling Tools Used in the Case Study An updated scoring system was used to re-e valuate BEM tools used in the case study (Figure 4-11). The scoring system was based on the one us ed in the initial evaluation. Adjustments were made based on information gathered during the case study. The four criteria used in the re-evaluation were user friendliness, interoperability, versatility, and speed. Versat ility encompasses the range of both available inputs and available outputs (which were in dividual criteria in the initia l evaluation). The criterion of speed was added to the re-evaluation. This criterion refers to calculation speed, that is, the amount of time that each BEM tool took to complete each of the three simulations. The comprehensive score for each BEM to ol was calculated as the sum of the individual criterion scores. Ea ch criterion was scored as the fraction of subcriteria supported by the BEM tool over the total number of subcri teria. For the criterion of interoperability, nine subcriteria were included in the checklist. Thus, the criterion score was calculated as x/9, wher e x = number of subcriteria supported by the BEM tool. Similarly for user-friendliness, which held 11 sub-criteria, the criterion score was determined as x/11. The criterion score fo r versatility was calculated as x/47 (for 47 67

PAGE 68

subcriteria), and the criterion score for speed wa s calculated as x/6 for (six subcriteria). The highest possible score for each criter ia was 1.00, and t he highest possible comprehensive score was 4.00. The cumula tive scores used in the BEM tool reevaluation were calculated by first converti ng the criteria scores into percentages. The final score ( c ) was the sum of these percentages with each criterion receiving the same weight. The final score was calculated by equation [4-2]: c = c1+c2+c3+c4 [4-2] where: c1 = Interoperability; c1 = x/9, c2 = User Friendline ss; c2 = x/11 c3 = Versatility; c3 = x/47, c4 = Speed; c4 = x/6, x = number of subcrite ria supported by BEM tool for the respective criterion Figure 4-11. Re-evaluation scoring system with criteria and subcriteria 68

PAGE 69

Based on the un-weighted resu lts from the re-evaluatio n, IES appeared to be the strongest of the three BEM tools used in the case st udy. This was largely due to IES receiving high marks in user-f riendliness (score 0. 73 out of 1.00) and versatility (0.91 out 1.00). Fi gure 4-12 illustrates the un-w eighted comprehensive scores obtained by the re-evaluatio n. As mentioned, IES appeared to be the strongest with cumulative score of 2. 75 out of 4 possible points. Green Building Studio had the second highest score of 2.41 out 4; and Ecotect had the lowe st score of the three with a total of 2.14 out of 4. Figure 4-12. Re-evaluation un-weighted cumulative scores A matrix was developed applying various weight s to the criteria based on the users order of importance. T he criterion first in im portance was multiplied by a factor of four, second by a factor of three, thir d by a factor of two, and four th by a factor of one. This matrix yielded 24 possible combinations (Table 4-3). 69

PAGE 70

Among the 24 possible weightings, IES achieved the highest score of 21. Based on the research findings Green Buil ding Studio is re commended when speed is the highest priority for the user, and interoperability the second highest; and when the order of importance is speed, user-f riendliness, interoperability, and versatility. The study recommends IES for any other combination of the criteria. Table 4-3. Re-evaluation matr ix with various weightings Order of Importance Software selection 1 2 3 4 Weight 1 Interoperability Userfriendliness Versatility Speed IES Weight 2 Interoperability Userfriendliness Speed Versatility IES Weight 3 Interoperability Versatility Userfriendliness Speed IES Weight 4 Interoperability Versatility Speed Userfriendliness IES Weight 5 Interoperability Speed Versatility Userfriendliness IES Weight 6 Interoperability Speed Userfriendliness Versatility IES Weight 7 Userfriendliness Interoperability Versa tility Speed IES Weight 8 Userfriendliness Interoperability Speed Versatility IES Weight 9 Userfriendliness Versatility Interoperability Speed IES Weight 10 Userfriendliness Versatility Speed Interoperability IES Weight 11 Userfriendliness Speed Interoperability V ersatility IES Weight 12 Userfriendliness Speed Versatility Interoperability IES Weight 13 Versatility Interoperability Userfriendliness Speed IES Weight 14 Versatility Interoperability Speed Userfriendliness IES Weight 15 Versatility Userfriendliness Interoperability Speed IES Weight 16 Versatility Userfriendliness Speed Interoperability IES 70

PAGE 71

Table 4-3. Continued Order of Importance Software selection 1 2 3 4 Weight 17 Versatility Speed Interoperability Userfriendliness IES Weight 18 Versatility Speed Userfriendliness Interoperability IES Weight 19 Speed Interoperability Userfriendliness Versatility GBS Weight 20 Speed Interoperability Versatility Userfriendliness GBS Weight 21 Speed Userfriendliness Interoperability Versatility GBS Weight 22 Speed Userfriendliness Versatility Interoperability IES Weight 23 Speed Versatility Interoperability Userfriendliness IES Weight 24 Speed Versatility Userfriendliness Interoperability IES Various weightings were based on multipliers applied to the order of importance for each criterion. The first most important criterion score was mu ltiplied by a fa ctor of four, the second most important multip lied by a factor of three, th ird most important multiplied by a factor of two, and fourth most important mult iplied by a factor of one. A detailed set of results from the re-evaluation is shown in Tables 4-4 through 4-7. This provides potential BEM us ers with a breakdown of the re-evaluation in terms of availability of each subcriteria used in the scoring system. Users may refer to this table to ensure that certain desired functions are in cluded in the BEM tool they select. This table served as a checklist during the re-eval uation. For each subcriteria, the BEM tool was scored with a 1 if the capabil ity is included in the softwar e, a 0 if it was not, and 0.5 if the capability was in cluded but with limitations. Ecotect demonstrated the highest degree of interoperability (Table 4-4) and obtained a score of 6.5 out of 9 possible points in the interoper ability evaluation criterion. IES had the second highest score (5.5 out of 9) and Green Building Studio demonstrated the lowest degree of interoperability (score 4 out of 9). Table 6 provides 71

PAGE 72

the checklist and scores for each of the three BEM tools for the criterion of interoperability. Table 4-4. Re-evaluation of th ree BEM tools for interoperability Subcriteria Ecotect Green Building Studio IES Geometry translation (from Revit Architecture as gbXML file) 0.5 0.5 0.5 Material translation (from Revit Architecture as gbXML file) 0.5 0 0.5 Openings (doors and windows) translation (from Revit Architecture as gbXML file) 0.5 0.5 0.5 Google SketchUp plugin 0 0 1 Import DXF 1 0 1 Import IFC 1 0 0 Import gbXML 1 1 1 Export gbXML 1 1 0 Export analysis data to Microsoft Excel 1 1 1 Total Points (out of 9) 6.5 4 5.5 Percentage score 0.72 0.44 0.61 For each subcriteria the BEM tool received 1 point if the capability was included, 0 points if not included, and 0.5 if the capability was included but with errors or limitations. The only program that Ecotect did not interoperate with was SketchUp. A potential strength of Ecotect was the ability to import IFC f iles. None of the other BEM tools had this capability. All three BEM tools allowed for gbXML files to be imported. However, the export of the BIM models from Revit as gbXML files to each of the three BEM tools showed errors in certain inputs. In all three software, errors were found in the geometry translation, material translation, and openings translation from the Revit models. When these inputs we re exported from Revit with errors, the BEM tool received a score of 0.5 on the checklist. Gre en Building Studio did not receive material data from the gbXML file (and thus received a 0 in this subcriteria) and these inputs had to be re-entered. IES, which re ceived the second highest score for 72

PAGE 73

interoperability provides a SketchUp plugin, but does not have the c apability to export gbXML files. All three BEM tools were able to export analysis data to Microsoft Excel.IES received the highest score out of the three BEM tools for user friendliness supporting eight out of the 11 subcriteri a (Table 4-5). Green Building Studio had the second highest score (6.5 out 11) and Ecotect had the lowest score of the three (6 out of 11). IES benefitted from the inclusion of a gbXML model error check, a secondary model error check that is run automatically before initializing simulations, and an automatic report generator. Table 4-5. Re-evaluation of thr ee BEM tools for user friendliness Subcriteria Ecotect Green Building Studio IES Help file 1 1 1 User support forum 1 1 1 Simple user interface 0 1 0 Default libraries / templates 1 1 1 gbXML import model error check 0 0 1 Model error check during simulation 1 0 1 Automatic report generator 0 1 1 3-D model GUI (graphical user interface) 1 0 1 Requires minimal expertise 0 0.5 0 Design alternatives assistance 0 1 0 Ability to edit building geometry in program 1 0 1 Total Points (out of 11) 6 6.5 8 Percentage score 0.55 0.59 0.73 For each subcriteria the BEM tool received 1 poi nt if the feature is included, 0 points if not included, and 0.5 if the feature was inclu ded but with limitations. In the re-evaluation, the versatility evaluat ion criterion was comprised of subcriteria in the categories of available inputs, versatil ity of inputs, availabl e outputs, and versatility of outputs. Availabilit y of inputs and outputs refers to the range of inputs and outputs provided by the BEM software. Versatility of inputs and outputs refers to the ability of users to define and customiz e the inputs and outputs. Ov erall, IES had the 73

PAGE 74

highest score (43 out of 47 po ssible points) and appeared to be the most versatile of the three BEM tools assessed in t he re-evaluation (Table 4-6). Ecotect was the second most versatile with a score of 41 out of 47, and Green Building Studio appeared to be the least versatile with a score of 23 out of 47. The scoring for each subcriterion was as follows: 1 if the input/output is included, 0.5 if the input/output included but with limited options, and 0 if the input/output is not included. Table 4-6. Re-evaluation of three BEM tools for versatility. Subcriteria Ecotect Green Building Studio IES Versatility of inputs User-defined constructions 1 0.5 1 User-defined occupancy schedule 1 0 1 User-defined equipment/lighting schedule 1 0 1 User-defined systems (HVAC) 1 0 1 User-defined time step for calculations 0.5 0.5 0.5 Zone-by-zone inputs 1 0 1 Model builder 1 0 1 Versatility of outputs User-defined time step 0.5 0 0.5 User-defined reports/graphical outputs 1 0 1 Graphical analysis over model 1 0 1 Animations 0 0 1 Room/zone level analysis 1 0 1 Graphical comparisons between design iterations 0 1 1 Available Inputs HVAC type 1 1 1 Heat recovery system 1 0 0 Glazing specifications (low-e, tint, U value, visible transmittance 1 1 1 Automated lighting controls 1 1 1 Constructions (walls, roof, floor) 1 1 1 Albedo 1 1 1 Shade walls / louvers 1 0 1 Lighting power density 1 1 1 HVAC design flow 1 0 1 74

PAGE 75

Table 4-6. Continued Subcriteria Ecotect Green Building Studio IES Local terrain 1 1 1 Geographic location / climate 1 1 1 Occupancy schedule 1 0 1 Equipment / lighting schedule 1 0 1 HVAC schedule 1 0 1 Required interior design temperature (heating / cooling setpoint) 1 1 1 Equipment power density 1 1 1 Fuel type 1 1 1 System energy efficiency 1 0 1 User-defined fan power 1 0 1 Operable window (openings to allow for natural ventilation) 0 1 1 Operable windows schedule 0 0 1 Available Outputs Energy usage 1 1 1 Carbon emissions 1 1 1 Resource management 1 1 1 Thermal analysis 1 0 1 Heating / cooling load breakdown 1 1 1 Solar analysis 1 0 1 Daylighting assessment 1 1 1 Lighting design 1 0 1 Lifecycle cost analysis 1 1 1 Ventilation and airflow analysis 1 1 1 Water usage 1 1 0 Design alternative comparisons 0 1 0 Total Points (out of 47) 41 23 43 Percentage score 0.87 0.49 0.91 For each subcriteria the BEM tool received 1 poi nt if the feature is included, 0 points if not included, and 0.5 if the feature was inclu ded but with limitations. The criterion of speed was ev aluated for the three BEM tools used in the case study by recording the amount of time eac h BEM tool took to perform each simulation (energy, daylighting, and natural ventilation). Results are shown in Figure 4-7. Green Building Studio received the highest score for speed with 6 out of 6 possible points. IES received the second highest score (3 out of 6) and Ecotect received the 75

PAGE 76

lowest score (0 out of 6). The major advant age of Green Building Studio in regard to this criterion had to do with it s calculation engine being serv er based. Calculations were performed online which decreased calculat ion times in all three analyses types. IES, which had the second highest score was able to perform the three simulation types in under 1 hour. Ecotect, which had the lowest score for speed, had simulation times that lasted several hours. Table 4-7. Re-evaluation of three BEM tools for speed Subcriteria Ecotect Green Building Studio IES Energy simulation time under 1 hour 0 1 1 Energy simulation time under 10 minutes 0 1 0 Daylighting simulation time under 1 hour 0 1 1 Daylighting simulation time under 10 minutes 0 1 0 Ventilation simulation time under 1 hour 0 1 1 Ventilation simulation time under 10 minutes 0 1 0 Total Points (out of 6) 0 6 3 Percentage score 0 1.0 0.5 4.4 Guidelines for using Ecotect, Green Building Studio and IES During the case study, a log was mainta ined noting problems and observations for each of the three BEM tools us ed in the case study. The st eps in the energy modeling process that were analyzed were model pr eparation in Revit, model preparation in BEM tool, weather data, energy analysis, day lighting analysis, ve ntilation analysis, and schedule implementation. The following section summarizes these observations (which are provided in full detail in t he Appendix C) for each BEM tool. 76

PAGE 77

4.4.1 Model Preparation in Revit During this step of the energy modeling process, it was im portant to check that all rooms were modeled correctly and bounded by the correct elements in plan and section. If there were errors in how the rooms were modeled, Re vit did not allow the BIM model to be exported as a gbXML. Problems encountered during this stage of the energy modeling process in cluded the following: Inconsistent phase assignments betw een room elements and other building elements Overlapping rooms Overlapping room-bounding objects Missing objects (e.g. shade walls) in the gbXML model Special attention should be given to rooms and room-bounding objects. It is important to ensure that all interior spaces are model ed as rooms; otherwise gbXML will recognize these as exterior spaces. 4.4.2 Model Preparation in Build ing Energy Modeling Software The model preparation portion of the energy modeling process refers to the work that was done on the model bet ween importing gbXML files to the BEM, and initializing the simulation in BEM. The amount of inputs needed in model preparation for each BEM software varied. Green Building Studio, whic h did not have model-building functions, required minimal inputs to run a base simu lation. Model preparation in Ecotect and IES required users to run error checks before simulations could start. Both BEM tools have model-building functions that allow users to fix model errors. Automatic error reports were generated by both BE M tools and allow users to lo cate errors in the model with relative ease. 77

PAGE 78

Green Building Studio requi red the least amount of model preparation before running the initial simulation. The gbXML models of Rinker Hall and Gerson Hall exported from Revit were loaded directly into the Green Building Studio web-based analysis engine. In the online interface, user s fill out a questionnaire about building type and location before the base simulation can run. After running a base simulation, iterations of the build ing model can be run by adjusting the building s pecifications in the project defaults tab. In th is window, building specificat ions related to system types, constructions, and glazing are input. While this allows for simulations to run quickly and require minimal inputs, it limits the amount of editing a user may perform on the building model in Green Building Studio. Any changes to the building geomet ry and the interior organization of zones must be performed in Revit (or other gbXML-enabled BIM or 3D modeling platform). While gb XML models may be inspected using a thirdparty 3D model viewer, Green Building Studio is un able to edit potential building geometry errors that occur during the translati on of the BIM model to gbXML file. Model editing proved to be useful in Ec otect and IES as many errors were found in the gbXML file s. This capability is enhance d in both tools by including error detections. Ecotects error detection occu rs when the first simulation is initialized and provides a list of errors detected and corresponding location in the model (e.g. zone28, surface2093). IES performs its e rror detection when the gbXML file is imported. IES error reports also inclu de corresponding locations in the model to the errors found. The most common error in both programs during the case study was errant holes in surfaces. Other major erro rs encountered in the gbXML files imported into the BEM tools were missing components, such as shade walls. Such components 78

PAGE 79

were rebuilt in Ecotect and IES. Due to limitations of the software, these components were omitted from the Green Build ing Studio energy models. Like Green Building Studio, Ecotect and IES al so may require to re-input envelope constructions. Both Ecotect and IES support a greater degree of versatility in specifying envelope constructions by allowing users to specify construction layers and layer properties. This is in contrast to Green Building Studio, which only allows users to specify construction types included in a drop down menu. 4.4.3 Weather Data Acquisition The proximity of weather dat a sources to actual building locations for the three BEM tools ranged from 4. 0 miles to 0.8 miles (Figure 4-13). To obtain Gainesville weather data, the weather file for Ecotect had to be downloaded from the DOE EnergyPlus website. By comparison, Green Building Studio and IES had weather data libraries with Gainesville weat her data already built into the software. Figure 4-13. Location of weat her data for three BEM tools in proximity to case study buildings 79

PAGE 80

In Ecotect weather data for Gainesville was loaded from the DOE Energy Plus website. The Gainesville weather data f ile came from information gathered at the Gainesville Regional Airport (located roughly 4 miles from the University of Florida campus). This was also the location of the weather data file for IES. By comparison, the weather data file acquired for Green Building Studio came from a weather database locat ed on the University of Florida ca mpus and much closer to the actual building locations. 4.4.4 Schedule Implementation The three BEM tools allow users to implement schedul es with varying degrees of customization. In particular the research sought to implement schedules for occupancy, equipment usage, electrical lig hting usage, and natural vent ilation. While Green Building Studio was only able to implement an oc cupancy schedule, Ecotect and IES were able to implement all four with varyi ng degrees of customization. Both Ecotects and IESs schedule editors a llow the user to create pr ofiles on the daily, weekly, and annual basis. Both BEM tools provide default schedule that may be used as a template and tailored to more specific conditions and schedules on the project. Ecotect: Ecotect allows users to implement all four of the schedules (also called operational profiles in Ecotect). The schedule library provides several typical operational profiles that may be adjusted in the schedule edito r (Figure 4-14). Using the schedule editor, hourly operational profiles may be created for occupancy, equipment usage, electrical lighting, and natural ventilation. Users can click and drag points on the hourly operational profile to adjust and create new schedules, or input the values into the table. 80

PAGE 81

Figure 4-14. Ecotec t Schedule Editor Each of these schedules was implemented for all zones using adjusting the zone properties. Number of occupants and occupancy schedules were input under the tab occupancy. A generalized schedule assuming el ectrical lighting and room equipment run at the same time was i nput under the tab internal ga ins. The ventilation schedule was developed using the guidelines set fo rth by ASHRAE Standard 55.2004 (Figure 415). Under the tab infiltration rate, the study referred to t he weather file to develop a natural ventilation schedule that was active fo r outdoor temperatures that fall within the ASHRAE Standard 55.2004 thermal comfort range. As per Standard 55.2004, a wider comfort range is allowed when relying on nat ural ventilation. This meant that the ventilation schedule was developed so as to trigger the operable windows to be 100% open during the times of year when the outdoor temperature was within the acceptable comfort range for natural ventilation. Usi ng the weather data pr ovided by Ecotect, the schedule was developed by identifying those ti mes of year and manually inputting them into the operable window schedule. 81

PAGE 82

Figure 4-15. Mean monthly average tem peratures and corresponding comfort ranges. The shaded area refers to acceptable air-conditioned thermal comfort ranges, and the black lines refer to acceptable thermal range for nat ural ventilation. Dotted lines denote the acceptable t hermal comfort range for given mean monthly outdoor temper atures (ASHRAE 2004). Green Building Studio: The only schedule that coul d be implemented into the energy models in Green Building Studio was the occupancy schedule. The option School, year-round was selected from a drop down menu during the initial questionnaire when the gbXML file was init ially imported into Green Building Studio. Users are unable to create thei r own schedules, or adjust occupancy schedules in the drop down menu. For this reason, Green Building Studio is not recommended for calibration purposes. IES: Schedule is handled in the Apache module with the icon for Apache profile database manager. Each room has been assigned a pr ofile from a drop down menu in the ModelIT module. These can then be customized by editing the profiles in Apache. Users may create thei r own schedules here as we ll allowing for a degree of customization (Figure 4-16). Th is allows users to input va lues in the schedule either graphically or numerically. 82

PAGE 83

Figure 4-16. IES schedule editor interface This was especially useful when develop ing operational profiles for operable windows. Unlike Ecotect, which required users to develop daily schedules based on climate data, IESs schedule (profile ) editor allows users to devise schedule based on formulas as well using the modulating formula profile creation tool (Figure 417). In this way, the operable window sc hedule was input by triggering operable windows to open based on thermal parameter s. These were input as temperature ranges derived from ASHRAE Standard 55.1. Operable windows were open 100% when the outdoor temperature was less than 78 F and greater than 70 F. 4.4.5 Energy Analysis Each BEM tool reported energy usage in different ways and had varying ranges of capabilities. The extent to which users ar e able to customize reports and energy analyses varied as well. Green Building Stud io, which was the quickest to generate energy reports, was limited in output options. Ecotect and IES provided more versatility in outputs, but had longer calculation times (u nder one hour calculation times for IES while Ecotect calculations c ould take several hours). In particular, 83

PAGE 84

IES and Ecotect differed from Green Bu ilding Studio by allowing users to specify thermal zones within the model and simulation time steps for energy analysis. Figure 4-17. IES Modulating formula pr ofile creation interface allows schedules to be derived from thermal parameters. Ecotect: Ecotect runs energy anal yses through the drop down menu Calculate >> Thermal Analysis, and result s are viewed in t he Analysis module under the tab Resource Consumption. Simulati ons and reports may be broken down into daily time steps and on a zone-by-zone bas is (depending on which zones are selected for the simulation run). Various outputs may be selected, displayed and compared within the analysis tab. These outputs included: Hourly temperature profile Hourly heat gains/losses Heating/cooling loads Daily to annual energy use Daily load matching Hourly solar collection Hourly to annual electric use Hourly to annual natural gas use Hourly to annual coal use 84

PAGE 85

Hourly to annual fuel oil use Hourly to annual kerosene use Green Building Studio: Energy analyses in Green Building Studio may be viewed in either the overall report for each si mulation run, or in t he data run charts which compare the energy performance for different runs and projects (F igure 4-18). The run charts break down the energy us age into nine categories: Area lights Exterior usage Miscellaneous equipment Space cooling Heat rejection Vent fans Pumps auxiliary Space heat Hot water Figure 4-18. Green Building Stud io run chart comparing buildings used in case study IES: Thermal analysis was conducted using the Apache module for calculations, and the Vista module for result s analysis. Users should make sure to run an update of the SunCast calculations bef ore performing energy analyses in Apache. The Apache Module provides the interfac e to specify constructions, systems, and 85

PAGE 86

schedules. The Apache Dynamic Simulation was selected and was run from Jan 1 to December 31 with a 15-minute time step (by default). This specifies that the simulation will calculate values for the entire year, with a resolution bas ed on 15 minute intervals. Similarly with the Analysis tool from Ecotec t, the Vista Module for IES provides users with the ability to cust omize reports and the presentat ion of data. The project summary base report generated by IES for energy analysis breaks down the annual energy usage into t he following categories: Heating Cooling Fans / pumps Lights Equipment 4.4.6 Daylighting Analysis Each BEM tool uses a different methodology for assessing daylighting performance. The range of outputs differed as well. Green Building Studio provided outputs in the units of glazi ng factor, while Ecotect and IES provided outputs in daylight factor (the inverse of glazing factor). Ecotect and IES were also able to provide graphical outputs wit h daylight factor analysis grid s displayed over the floor plan. None of the software allo w the user to specify the date and time at which the daylight simulation is perform ed, and only Ecotect and I ES allow the user to specify the placement of sensor points. All three BEM tools were set to CIE uniform sky conditions for all simulations. Ecotect: While the versatility of Ecotects daylight simulation inputs were limited (because Ecotect was unable to spec ify the date and time of the simulation), users are able to customize both the analysis grid and pres entation of daylight factor 86

PAGE 87

data in analysis graphs and reports. The analysis grid is helpful for users to locate areas in zones that do not have adequ ate daylighting (2% daylight fa ctor by LEED standards), and provides graphical cues as to where to place electrical lighting efficiently, and alternative glazing strategies to improve daylighting performance. One disadvantage of Ecotects daylighting simulation engine is that it was not uncommon for simulation runs to take several hours. Using simplif ied models can reduce the calculation time. However, it was noted that reduc ing the complexity of the gbX ML file export from Revit led to more errors in the model upon importing it into Ecotect. Green Building Studio: Glazing Factor (inverse of daylight factor) is the parameter that Green Buildi ng Studio uses to assess daylighting performance. Results are broken down on a zonal basis. Green Building Studio does not allow the user to specify sensor posit ions in the model. This is a major disadvantage for users simulating daylighting performance for specific areas within zones (e.g. the location of a desk). Users are also unable to specify the dat e and time of the simulation run. Without any of this information, it is difficult to utilize a single simulation runs daylighting data. These reports are useful to compare design alternatives. While daylighting analysis in Green Building Studio is not very versatile, simulation runs are much quicker, only taking a matter of seconds (dependent on user bandwidth). The daylighting results are also tailored to show effectiveness of t he buildings daylight performance compared to the requirements for LEED credits. These credi ts are awarded if the building is able to provide a glazing factor of 0. 02 for at least 75% of the r egularly occupied floor area. IES: Daylighting was performed using t he FlucsDL module in IES. Prior to running the FlucsDL simulation, the SunCast module wa s used to update the 87

PAGE 88

shading calculations. Graphical outputs called daylight gradients ov er the floor plans were very helpful to locate errors in the model. The daylight gradients are similar to Ecotects analysis grid displaying color gradients to daylight factor values gridded over the floor plan. The height of t he grid can also be specified and by default is set at the height of a typical working plane. As wit h the energy models in Ecotect and Green Building Studio, shading devices for Rinker Ha ll were lost in the gbXML file import and had to be modeled again in IES< VE>. Daylighting simulations can be run for any hour of any day throughout the year. This allo ws for daylight autonom y to be calculated. 4.4.7 Natural Ventilation Analysis There is a wide range of capabilities for BEM tools in the category of natural ventilation. Potential energy savings from nat ural ventilation could be calculated using all three BEM tools used in the case study. Howe ver, since the use of natural ventilation and its resultant energy savings are depend ent on the unpredictable variables of weather and occupancy behavior (i.e. opening operable windows), natural ventilation simulations must make broad generalizations and assumptions. All three software rely on the Sherman-Grimsrud ventilation method to calculate natural ventilation potential. This calculation is based on hourly wind spe ed and indoor versus outdoor temperatures to model air change. ASHRAE Standard 55 was used to determine adequate monthly comfort ranges. Users should note that this standard affords a wider range of thermal comfort when relying on natural ventilation for cooling based on changes in occupants thermal sensation or adaptive therma l comfort. A study conducted by ASHRAE revealed that occupants, due to psychological factors, have a wid er thermal comfort range when relying on natural ventilation. 88

PAGE 89

Green Building Studio: By default, natural ventilation simulations in Green Building Studio were set to the following conditions, which could not be changed by the user: Building and openings are designed to al low for the stack effect and/or cross ventilation Natural ventilation is used during the rmal comfort zone periods (GBS does not specify what the thermal comfort zone is). Air changes per hour is less than 20 ACH Entire window area is operable Based on these assumptions and local climat ic conditions, Green Building Studio provides a concise report on natural vent ilation potential. This report includes the following outputs: Total hours mechanical cooling required Possible natural ventilation hours Possible annual electric energy savings Possible annual electric cost savings Net hours mechanical cooling required These values are averaged over the entir e building and cannot be broken down on a zone-by-zone basis. Green Building Studio also does not provide a platform to conduct microclimate analysis within zones usi ng computational flui d dynamics (CFD) to simulate airflow through spaces. Ecotect: Users are able to estimate potent ial energy savings from natural ventilation by comparing two energy si mulation runs: one without operable windows activated and one with operable wi ndows activated by assign ing an operational profile (schedule) to operable windows. The developm ent of the operable window schedule can be informed by climate data r eports that indicate days throughout the year when the climate is within the comfort range. This can was done by se lecting temperature from the thermal analysis tool. This created a graph that displayed indoor and outdoor 89

PAGE 90

temperatures. When the outdoor temperature is within the ASHRAE 55-defined comfort range, operable windows may be open to reduce the cooling demand. During these periods in the operational pr ofile, the user may specify percentage of the window area that should be open. As a standalone software, Ecotect does not pr ovide users with a platform to conduct microclimate CFD analysis, although it is possible to use a third party software such as WinAir to conduct CFD simulations. This file may be brought back into Ecotect and used in the analysis grid to provide users wit h a visualization of air flow through zones. IES: Using a methodology similar to the one described in the previous section on natural ventilation in Ecotect, users may also estima te potential energy savings from natural ventilation in IES< VE>. Two simulation runs are needed, one without operable windows, and one with operable windows activated. The difference between the two is the potentia l energy savings from nat ural ventilation. A major advantage to IES is that the operab le window schedule can be defined by thermal parameters (Figure 20). Furthermo re, IES also provides zonal CFD analysis providing outputs of average cubic f eet per minute (CFM) as a rate of outdoor air entering the building (infiltration). Calcul ations are run in the Apache module and windows are assigned opening properties using the MacroFlo module. Within MacroFlo, glazing on external walls can be sele cted and adjusted to be up to 100% open. 4.4.8 Results Analysis in the Building Energy Modeling Tools For analyzing results, the th ree BEM tools carried varying ranges of capabilities. While Green Building Studio was able to out put a comprehensive report very quickly, the other two (Ecotect and I ES) provide more detailed analysis tools to help users interrogate the results. Users requiri ng rapid report outputs fo r several areas of 90

PAGE 91

building performance may find Green Buildi ng Studio more favorable; meanwhile users requiring more detailed analysis and cont rol over how data is displayed will find Ecotect and IES more suitable. 4.5 Guidelines for Using Building Energy Modeling The following sections provide guidelines and recommendations for selecting and using BEM for the analysis of high performance buildings Section 4.5.1 provides guidelines for utilizing BEM and provides recommendations for BEM application in various phases of the building lifecycle. Se ction 4.5.2 provides potential BEM users with guidelines for selecting the appropriate BEM tool. Intended us ers of the guidelines are beginner energy modelers. These may in clude building designers and green building consultants. The guidelines are based on obs ervations made during the case study. As such, the guidelines are ta ilored to the energy modeling methodology used in the research as illustrated in Figure 4-19. Figure 4-19. Workflow of energy modelin g methodology employed in case study Potential BEM users are enc ouraged to use the guideli nes as a template for developing their own energy modeling me thodology and energy modeling software criteria for evaluation and selection. Adaptations to t he energy modeling methodology 91

PAGE 92

and guidelines for BEM selection presented in the following sect ions are necessary based on the particular requirements and exis ting workflows of individual users. 4.5.1 Guidelines for Building Energy Modeling Application The following section provides potential BEM users with guidelines on how to go through the energy modeling process. Based on the methodology used in this research the energy modeling process is brok en down into three primary stages: 1) Develop BIM models using a gbXML-enabled BIM platform 2) Develop a baseline energy model based on ASHRAE Standard 90.1 3) Integrate energy efficiency measur es for energy model optimization. The research recommends developing BIM models in a gbXML-enabled BIM platform. Assuming the BEM tool is interoper able with BIM via gbXML file, the amount of model preparation time should be reduced because the building geometry does not need to be recreated in the BEM software. Other information shared between BIM and BEM may include glazing and building envelo pe constructions. Exported gbXML files from the BIM platform should be relatively si mple in order to reduce calculation times. In Revit, the complexity of the gbXML file export may be specified. When the gbXML file is imported into the BEM tool, a gbXML file error check should be run to locate and fix potential model errors that occur in the interoper ation between BIM and BEM. While the BIM model is being developed, BEM users should also gather the necessary information for the required inputs to develop a baseline model. Typical inputs may include building geometry building envelope constructi ons, weather file for the closest available building location, HV AC type (refer to ASHRAE Standard 90.1 for baseline values for building type and climate region), lighting power density per building type, equipment power densit y per building type, occupancy loads and schedules. 92

PAGE 93

These inputs are entered to generate a basel ine model. The outputs generated by the baseline model serve as benchmarks against which further design iterations may be tested in an effort to improve energy efficien cy. The results of the baseline model should be interrogated in order to identify energy uses that may be target ed to improve energy efficiency. For example, the simulations in the case study showed t hat a large proportion of energy was used for space cooling purpose s. This type of ener gy use could then be targeted for energy efficiency measures in order to make more significant impacts on the overall energy consumption of the building. Energy efficiency measures that could be implemented to reduce the cooling load include increasing the R-va lue of the building envelope, integrating natural ventilation when climatic conditions are favorable, and increasing the roof reflectance. The final stage of the energy modeling process involves developing and testing a series of energy efficiency measures to optim ize the energy model. Various iterations of the energy model incorporati ng different combinations of energy efficiency measures can be tested against the baseline model. T he percent energy savings against the baseline model can be used to compare the di fferent design iterations and to select the most energy efficient combination of energy efficiency measures. These iterations may also be used to compare models in a number of performance criter ia besides energy usage. Other performance parameters may include daylighting performance, lifecycle cost, carbon emissions, and resource management (water and building materials). The different iterations may also compare energy savings against initial and lifecycle costs. Based on the various capabilities of the th ree BEM tools, the research identified building lifecycle phases when these capabilities may prove useful to BEM users. The 93

PAGE 94

extent to which each BEM tool is able to supports the recommended capabilities is indicative of how useful the BEM tool is for each respective building lifecycle phase. Table 4-8 identifies ca pabilities that are useful during th e conceptual design phase. All three BEM tools appeared us eful for the conceptual design phase with each tool supporting ten out of the elev en recommended capabilities. Table 4-8. BEM tool use dur ing conceptual design phase BEM Capability Ecotect Gr een Building Studio IES Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Building geometry creation X X Orientation X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system selection X X X Design alternative assistance X Inclusion of capabilities that support the sp ecified use for each of the BEM tools is indicated by X. Table 4-9 identifies capabi lities that are useful during the design development phase. All three BEM tools appeared useful for design devel opment with Ecotect and IES supporting all ten of the re commended capabilities and Green Building Studio supported nine out of the ten. Table 4-9. BEM tool use during design development phase BEM Capability Ecotect Gr een Building Studio IES Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system refinement X X X Resource management X X Lifecycle cost analysis X X X 94

PAGE 95

Inclusion of capabilities that support the sp ecified use for each of the BEM tools is indicated by X. Table 4-10 identifies capabilit ies that are useful during the construction documents phase. Ecotect and IES appeared more useful than Green Building Studio for this building lifecycle. Both Ecotect and IES supported f our out of the five recommended capabilities, while Green Building Studio only supported two. Table 4-10. BEM tool use duri ng construction documents phase BEM Capability Ecotect Gr een Building Studio IES ASHRAE Standard 90.1 compliant energy use estimate for LEED credit / code compliance Glazing type and specifications input X X X Building envelope material selections (user-defined layers) X X Material schedule assistance X X Lifecycle cost analysis X X X Inclusion of capabilities that support the sp ecified use for each of the BEM tools is indicated by X. Table 4-11 identifies capabilit ies that are useful during the construction and contracting building lifecycl e phase. IES appeared to be the most useful BEM tool for this building lifecycle phase s upporting four out of the four recommended capabilities. Ecotect was the second most useful supporting three out of the four functions, and Green Building Studio was the least useful supporting one out of the four. Table 4-11. BEM tool use during construction and contracting phase BEM Capability Ecotect Gr een Building Studio IES Building material/component supplier selection X X Glazing supplier selection X X X Material documentation for LEED credit X X HVAC design and sizing X Inclusion of capabilities that support the sp ecified use for each of the BEM tools is indicated by X. 95

PAGE 96

Table 4-12 identifies capabilit ies that are useful duri ng the facilities management (building operation) build ing lifecycle phase. IES appeared to be the most useful BEM tool for this building lifecycl e phase supporting five out of the five recommended capabilities. Ecotect was the second most useful supporting three out of the five functions, and Green Building Studio was the least useful supporting none of the four. Table 4-12. BEM tool use dur ing facilities management phase BEM Capability Ecotect Gr een Building Studio IES Model calibration (operat ional profiles) X X Model calibration with plant data X Energy and cost benefits for changes to lighting systems X X Energy and cost benefits for changes in HVAC system operation X Energy and cost benefits for building envelope chagnes X X Inclusion of capabilities that support the sp ecified use for each of the BEM tools is indicated by X. Based on the capabilities provided by each BEM tool, tables 4-8 through 4-12 suggest that Ecotect and IES are us eful BEM tools from conceptual design phase to facilities management phase, whil e Green Building Studio is recommended for use in early design stages (conceptual design and design development). Based on Ecotects capabilities, it appeared useful from the conceptual design phase through facilities management. Green Building Studio appeared to be useful primarily in early design stages (conceptual design and design dev elopment), but with lim ited applicability to more detailed design stages, construction phases, and facilities management. This is largely due to Green Building Studio havin g limited versatility in inputs and outputs. These limitations make it very difficul t to calibrate energy models. IESs capabilities appeared useful for a ll building lifecycle phases fr om conceptual design to 96

PAGE 97

facilities management. By providing inputs for MEP models and actual plant data, IES seemed to have increased utility dur ing later building lifecycle phases when compared to the other two BEM tools. 4.5.2 Guidelines for Building En ergy Modeling Software Selection The primary application of many of the BEM tools investigated in this research was using BEM as a design tool to aid in the development of greener de sign iterations. As such, the intended users of the guidelines are buildi ng designers and green building consultants. Existing BEM tools are diverse in terms of capabilities, inputs, outputs, and applicability to various building lifecycle phase s. The following guidel ines are meant to assist potential BEM users in selecting the appropriate BEM tool for the users intended BEM application. The BEM se lection process includes: 1. Define the building lifecycle phases for which the BEM tool is intended to be utilized. 2. Define the required inputs as necessary to utilize the BEM for t he specified building lifecycle phase applications, and use these as a checklist of pre-requisites. 3. Define the required ou tputs and use as a check list of pre-requisites. 4. Rank other criteria for BEM selection (i .e. interoperability, user friendliness, and speed) in order of importance. 5. Apply appropriate weights to the criter ia (based on order of importance) and score the BEM tools that meet the pre-requisites defi ned by steps 1 through 3. Potential BEM users should first define t he building lifecycle phases for which the BEM tool will be utilized. Certain BEM tools are geared only towards early design stages while others carry a wide r ange of capabilities and may be useful from conceptual design to facilities management. The range of a BEM tools available inputs is indicative of its applicability to various building lifecycle phases. 97

PAGE 98

Secondly, BEM users should ensure the necessary inputs are included for the intended building lifecycle phases that the user intends to apply BEM. For instance, BEM users intending to apply BEM to later building lifecycle phases such as facilities management should refer to Figure 7 in sect ion 4.1.3 Available Inputs (results for available inputs from t he initial evaluation) to make cert ain that the BEM tool provides inputs for occupancy schedule, lighting schedule, equipment schedule, and plant data. The degree of versatility of schedule implementation is pa rticularly important. The capability of user-defined schedules is a nece ssity for calibrating the energy model with actual data obtained from building operation. Recommended required inputs for different building lifecycle phases are ill ustrated in Table 4-13. Thes e inputs may be treated as pre-requisites to later BEM selection criteria. Table 4-13. Recommended requir ed inputs for BEM simulations in the different building lifecycle phases Conceptual design Design development (in addition to those included in conceptual design) Construction documents (in addition to those included in design development) Construction and contracting (in addition to those included in construction documents) Facilities Management (in addition to those included in construction documents) Building geometry Glazing type User-defined glazing specifications MEP model Customizable occupancy schedule Orientation Lighting power density User-defined envelope construction layers and properties Water efficient fixtures Customizable lighting schedule Weather file Equipment power density Customizable equipment schedule Envelope constructions Occupancy schedule Plant data Openings Lighting schedule HVAC fan power HVAC type Equipment schedule HVAC system levels Building type (function) Fuel type Energy/utility rates (cost) Operable windows Operable window schedule 98

PAGE 99

System energy efficiency Albedo Thirdly, BEM users should define a set of required outputs. These may serve as pre-requisites to later BEM selection criter ia. The required outputs may differ from user to user. After developing a checklist of requi red outputs, BEM users may refer to Figure 4-5 in Section 4.1.4 Available Outputs (resu lts for available outputs from the initial evaluation) to ensure that the potential BEM tool in cludes the required outputs. After narrowing down the potential BEM tool s based on the users required inputs and outputs, other cr iteria may be integrated into the selection process. Other potential criteria for evaluation may t hen be ranked in the users order of importance. Other criteria, such as those used in this research, may include user friendliness, interoperability, and calculation speed. Based on the users or der of importance to these criteria, appropriate weightings may be appl ied for scoring purposes. For example, the most important criterion may multiply the respective score in the initial evaluation by three; the second most important criterion may multiply the score by two; and the third most important criterion may multiply the respective score by one. The weighted scores may then be added together to prov ide a cumulative score that should indicate the most appropriate BEM tool for the users specified BEM applications. The BEM software selection process is syn thesized with corresponding tables for required inputs, (user-defined) required outputs, and examples of other soft criteria for evaluation (e.g. interoperability and user fr iendliness) in Figure 4-20. Potential BEM users are encouraged to use these guidelines as a template to develop their own BEM software selection system. The criteria and subcriteria are certain to vary from user to user. Particular users may require additional cr iteria and subcriteria to those used in this 99

PAGE 100

research. For example, the cr iterion of accuracy was not included in the scope of this research, but may be an important crit erion for potential guidelines users. Figure 4-20. Guidelines for BEM software selection 100

PAGE 101

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS By investigating existing BEM tools, the research prov ided insight on use of energy modeling, both in terms of practice and capabilities. In practice, the integration of BEM into building design, construction, and fac ilities management (st ill in development) will almost certainly lead to smarter, and increasi ngly energy efficient buildings. However, it remains to be seen how well t hese BEM tools perform for measurement purposes. Until then, the capabilities of BEM tools are limited in appl ication. The study recommends such BEM tools for use primarily in desi gn. The energy model may be used in an iterative workflow to impr ove energy efficiency against a baseline model and cautions users relying on BEM software to pr edict actual energy performance. 5.1 Conclusions The following section summarizes the conc lusions made during the research and is broken down based on the initia l objectives of the research. 5.1.1 Objective 1: Initial Evaluation Based on the literature review four major criteria were identified to evaluate 12 major BEM software. These criteria were in teroperability, user-friendliness, available inputs and available outputs. Based on these four criteria for evaluation, the study identified Autodesk Ecotect, Autodesk Green Building Studio, and IES as the top three out of the twelve evaluated. 5.1.2 Objective 2: Case Study The case study used the top three softwar e (Ecotect, Green Building Studio, and IES) to compare t he environmental performance of Rinker Hall (LEED Gold 101

PAGE 102

certified) and Gerson Hall (non-LEED certif iied) in three areas of environmental performance: energy usage, dayligthing, and natural ventilation potential. In energy usage, all three BEM tools simulated that Rinker Hall, the LEED Gold building, would consume less energy per square foot (energy use int ensity) and in total annual energy consumption (regardless of t he difference between the two buildings conditioned floor area). In daylighting performance, Rinker Hall again appeared to outperform Gerson Hall based on the selected rooms us ed in the case study. Although there were discrepancies in the results between the di fferent BEM tools used, in general Rinker Hall seemed to provide better daylighting to these regularly occupied spaces. Although the outputs of the three BEM tool s for natural ventilation potential were inconsistent with one another, each one simula ted that Gerson Hall was better designed to take advantage of natural v entilation than Rinker Hall. Si mulation results obtained by Ecotect and Green Building Studio show ed that energy savings due to use of natural ventilation were larger for Gerson Ha ll than for Rinker Hall. IES, which was capable of simulating airflow through spac es, predicted that Gerson Hall had higher levels of air flow from natural ventilation. Results showed that Gerson Hall would have higher average rates of airflow per square foot than Rinker Hall. 5.1.3 Objective 3: Re-evaluation of BEM Tools Used in the Case Study Based on the improved and more detailed cr iteria for evaluation used in the reevaluation, the research i dentified IES as the top BEM tool when criteria are weighted evenly. From the user specified order-of-importance matrix, it was determined that Green Building Studio may be a better BEM selection for users with high priority 102

PAGE 103

on calculation speed. However, for most other criteria orders of importance, the study recommends IES. 5.1.4 Objective 4: Developing Guid elines for Using Building Energy Modeling Intended users of the guide lines are building desig ners and green building consultants. The guidelines were tailored to aid in BEM selecti on and application for specified building lifecycle phases. Based on the required BEM capabilities for each building lifecycle phase, it was evident that many of the BEM tools investigated in the study are appropriate for early design stages, while only a few (IES, Ecotect, and eQuest) may be useful for later design phases, construction and contracting, and facilities management. 5.2 Research Limitations As an evaluation of existi ng BEM tools, the research sought to develop a methodology that compared these tools in a relatively consistent manner. This proved to be very difficult as the existing BEM tools ar e very diverse with different intended users and applications. Thus, while the research atte mpted to develop criteria for evaluation that could fairly compare su ch diverse programs, these cr iteria are almost certainly tailored to a preconceived notion of BEM whil e the project was still in its developmental stage. 5.2.1 Objective 1: Initial Evaluation The initial cross evaluation relied on information gathered dur ing the literature review to fill out sub-criteria checklists for each criterion for ev aluation. The data was limited to available data and liter ature to fill out these check lists. Ideally, the study would have test driven each of the 12 BEM tools used in this portion of the study but was limited by time and software costs. This portion of the study also assumed an even 103

PAGE 104

weight applied to each cr iterion for evaluation. In order to select the top three BEM tools out of the 12 investigated, the study was limited at this portion to an even-weighted scoring system. 5.2.2 Objective 2: Case Study In the development of any energy model, a number of assumptions must be made. The number of variables that affect building energy usage are vast, so the model is reliant on a number of assumptions and conditi ons. These assumptions also varied from program to program based on the available inputs provided for each one. In all scenarios, the implem entation of schedules is alwa ys an approximation as it is impossible to predict the actual behavior of occupants and building operation practices. Ecotect and IES have c apabilities of implem enting increasingly accurate schedules that could be cust omized on a zone-by-zone basis. Meanwhile, generalized assumptions were made in Green Building Studio about occupancy and operation based on default values and aver ages for schedules for higher education building types. Similarly, values for li ghting power density and equipment power density were based on standard and averaged values per building type based on the ASHRAE 90.1 Standard (this was applied to all three BEM tools). These values along with corresponding schedules simulate approximations in regards to HVAC use and internal gains. Regarding daylighting performance (as per LEED requirements) CIE uniform sky conditions for simulation purposes were assu med in all three BEM tools. Ecotects daylighting calculations were limited to only taking daylight fa ctor data for December 21 (worst case scenario), while IESs da ylighting simulations were limited to September 21 (average case scenario). Gre en Building Studios daylighting simulation 104

PAGE 105

methodology is uncertain as all inputs and settings related to daylighting (except for glazing specifications) are automated. Because of thes e default and inconsistent daylighting simulation settings among the three BEM tools used in the case study, the research was limited to comparing the daylighting performance between the two buildings for each BEM tool individually, and could not compare the results between the different BEM tools. As previously menti oned, the study would ha ve ideally compared the daylighting performance in terms of day light autonomy instead of daylight factor. This calculation is preferred by the AEC community in that it accounts for daylighting performance throughout the year and describes daylighting as the percentage of time that spaces do not have to rely on electrical lighting. Daylight factor can be taken at any time leading to inconsistent simulati on practices throughout the industry. These inconsistencies are illustrat ed by the limitations of the three software, each of which calculate daylight factor at different times. Due to these limitations, the research was only able to assess daylight performanc e in terms of daylight factor. Similarly in the natural ventilation si mulations, no uniform simulation methodology could be established among the three BEM tools. This again limited the research to comparing the performance of t he two buildings within each BEM tool individually. Green Building Studios natural ventilation simulation was limited to default settings and values. In developing the operational prof ile for operable windows in Ecotect, the research had to rely on weather data and input operational values manually. The operational schedule used assumes that oper able windows are fully open during days when the outdoor temperatur e is within the ASHRAE St andard 55 comfort range. A similar assumption was made in the operat ional profile for operable windows in 105

PAGE 106

IES, which used a thermal parameter formula to trigger operable windows to be 100% open when the outdoor tem perature is between 70F and 78F. In all three BEM tools, the following as sumptions were made: Operable windows are 100% open during times of acceptable outdoor temperature The buildings are designed to allow for the stack effect and/or cross-ventilation to occur All windows are operable 5.2.3 Objective 3: Re -evaluation of the BEM Tools Used in Case Study The re-evaluation portion of the study opted to update the set of criteria for evaluation based on the observati ons from the case study. T he categories of available inputs and available outputs were combined into a single criterion, versatility. The subcriteria within versatility are also brok en down to assess the amount of inputs and outputs supported by each BEM tool, as well as the degree of resolution within each one. Speed was also added as another criterion in the re-evaluation as it was discovered that the time requi red for certain programs perf orming certain calculations was a major disadvantage to the software. Ideally the criteria for evaluation used in the re-evaluation would also have been used in t he initial evaluation phas e of the research. 5.2.4 Objective 4: Developing Guidelin es for Using Building Energy Modeling One of the major difficulties and limitations in developing the set of guidelines in this research was the fact that the obser vations made during the project (as summarized in Appendix B) were only based on the ener gy modeling methodology forged by a single user both learning and using t hese BEM tools for the first ti me. Many of the advantages, disadvantages, and complications associated with the three BEM to ols were based on subjective observations and BEM use (e.g. other users of the softwa re may not run into 106

PAGE 107

the same problems, or discover other probl ems, etc). The guidelines presented in the research are thus based on a single ener gy modeling methodology and workflow. 5.3 Recommendations for Future Research As Krygiel and Nies (2008) not e, the two primary ways in which BEM tools are utilized are for design and for measurement. While this research can remark on the applicability of BEM as a desi gn tool and for meeting simu lation-based LEED credit requirements, the accuracy of these BEM tool s remains to be assessed. In that regard, these tools are limited to ac ting only as design tools and for the sole function of improving environmental performance. Future research assessing the accuracy of these BEM tools, particularly thos e used in the case study, c ould be useful to provide recommendations to software developers, and could potentially improv e the faith in BEM users that buildings will meet intended performance requirement s. In particular, future research could focus on measuring simula ted energy usage against measured data for each of the two buildings us ed in the case study and co mpare energy use breakdowns. System levels and operational profiles (schedules) can be adjusted to calibrate the energy models with actual building operation. Another objective of future research c ould be a comparison of gbXML file-based energy models and IFC file-based energy m odels. As several model errors were discovered in the interoperability between Re vit and the BEM tools via gbXML file import/export, it would be useful to BEM user s to gain insight into which data schema contains less model errors. A couple of changes in the research met hodology would be made if the study were to be conducted again. For one, the more comprehensive crit eria for evaluation used in the re-evaluation would also be applied to the initial evaluation. As the research 107

PAGE 108

progressed through the case study phase, th e criteria for evaluation became more refined. Secondly, the study would have co mpared the daylighting performance for all regularly occupied spaces of the two buildings as opposed to selected rooms. In this way, a more accurate and comprehensive co mparison of the daylighting performance of the two buildings could be made. Finally, the criterion of accuracy should be added to the re-evaluation of the BEM tools. The objective of the future research will be to validate the accuracy of the BEM tools. An additional st udy comparing the data of simulated energy usage agains t measured data for the two bu ildings used in the case study is recommended for futu re research. The percent differences between simulated data and measured data could se rve as the basis for scoring the BEM tools in the accuracy criterion, and thes e scores can be added to those in the re-evaluation as an additional criterion for evaluation. 108

PAGE 109

109 APPENDIX A INITIAL EVALUATION

PAGE 110

Table A-1. lnteroperability subcriteria checklist and raw scores 110

PAGE 111

Table A-2. User friendliness sub-criteria checklist and raw scores 111

PAGE 112

Table A-3. Available inputs subcriteria checklist and raw scores 112

PAGE 113

Table A-3. Continued 113

PAGE 114

Table A-4. Available outputs checklist and raw scores 114

PAGE 115

115 Table A-4. Continued Table A-5. Cumulative score with respective criteria scores

PAGE 116

APPENDIX B CASE STUDY 116

PAGE 117

Table B-1. Annual Energy Usage Rinker Hall (output of Green Building Studio simulation) Energy, Carbon and Cost Summary Annual Energy Cost $90,956 Lifecycle Cost $1,238,824 Annual CO2 Emissions Electric 453.7 tons Onsite Fuel 49.9 tons Large SUV Equivalent 45.8 SUVs / Year Annual Energy Energy Use Intensity (EUI) 59 kBtu / ft / year Electric 687,488 kWh Fuel 8,601 Therms Annual Peak Demand 215.3 kW Lifecycle Energy Electric 20,624,649 kW Fuel 258,015 Therms Figure B-1. Rinker Hall energy use break down (output of Green Building Studio simulation) 117

PAGE 118

Figure B-2. Rinker Hall annua l fuel use breakdown (output of Green Building Studio simulation) Table B-2. Annual Energy Usage Gerson Hall (output of Green Building Studio simulation) Energy, Carbon and Cost Summary Annual Energy Cost $87,013 Lifecycle Cost $1,185,112 Annual CO2 Emissions Electric 440.7 tons Onsite Fuel 43.2 tons Large SUV Equivalent 44.0 SUVs / Year Annual Energy Energy Use Intensity (EUI) 78 kBtu / ft / year Electric 667,753 kWh Fuel 7,443 Therms Annual Peak Demand 218.9 kW Lifecycle Energy Electric 20,032,602 kW Fuel 223,282 Therms 118

PAGE 119

Figure B-3. Gerson Hall energy use break down (output of Green Building Studio simulation) Figure B-4. Gerson Hall Energy Use Br eakdown (output of Green Building Studio simulation) 119

PAGE 120

Table B-3. Natural Ventilation Gains Rinker Hall (output of Ecotect simulation) 120

PAGE 121

Table B-4. Natural Ventilation Gains Gers on Hall (output of Ecotect simulation) 121

PAGE 122

Table B-4. Continued Table B-5. Natural Ventilati on Potential Rinker Hall (out put of Green Building Studio simulation) Natural Ventilation Potential Total Hours Mechanical Cooling Required: 6,230 Hours Possible Natural Ventilation Hours: 1,370 Hours Possible Annual Electric Energy Savings: 32,254 kWh Possible Annual Electric Cost Savings: $3,677 Net Hours Mechanical Cooling Required : 4,860 Hours Table B-6. Natural Ventilati on Potential Gerson Hall (Out put of Green Building Studio simulation) Natural Ventilation Potential Total Hours Mechanical Cooling Required: 4,872 Hours Possible Natural Ventilation Hours: 1,000 Hours Possible Annual Electric Energy Savings: 50,645 kWh Possible Annual Electric Cost Savings: $5,774 Net Hours Mechanical Cooling Required: 3,872 Hours 122

PAGE 123

Table B-7. Natural Ventilation Airflow Rin ker Hall (output of IES simulation) Rinker Hall Room Designation Sq. Ft. avg. CFM 30 mech. 1608.5 44.1 30A elec. 301 8.7 106 Medium Classroom 955 24.4 110 Large classroom 1803 49.5 110A Elec 73 2.2 115 Student lounge 500 12.8 125 MEP Studio 1670 44.9 134 shower 85 1.4 136 shop 296 8.7 138 soils/conc. 734 18.4 140 structures studio 1331 35.9 140A Stroage 436 13.8 141 Interview 110 2.4 143 Interview 108 2.2 145 Men 235 5.3 146 Women 277.5 5.4 146A Mech Room 73 2.2 201 Tech 244.5 5.4 202 DES 586 14.5 203A Server Room 147 3.3 204 Plan Room 214 4.2 205 Janitor 29 0.8 206 Computer Lab 1191 29.2 207A Storage 76 1.4 208 Information Tech. 438 9.9 209 MCE 144 4.4 210 Medium Classroom 889 22.8 215 Medium Classroom 914 23.2 220 Medium Classroom 903 21.9 225 Medium Classroom 907 23 230 Medium Classroom 1006 25.1 235 Storage 236 6.3 235A Elec 32 0.5 238 Construction 1248 30.7 240 Est/Dwg/Sch 1437 34.6 245 Men 208 4.7 245A Storage 25 0.5 246 Women 256 5.8 246A Storage 23 0.5 301 Admin 170.5 3.7 302 Director Grad. 259 5.9 303 Main Conference Room 656 16.7 305 Main Office 998 22.5 123

PAGE 124

Table B-7. Continued Rinker Hall Room Designation Sq. Ft. avg. CFM 305A Office Mgr 142 3.1 306 Director 283 6.2 307 Associate Director 257 5.2 308 Storage 167 3.4 309 Faculty Office 153 3 310 Mail/Kit/Copy 255 5.1 311 Faculty Office 152 3 312 Conference Room 274 5.6 313 Resource Center 167 3.4 314 Faculty Office 153 3 315 Faculty Office 152 3 316 Faculty Office 153 3 319 Faculty Office 152 3 320 Grad Studio 363 7.5 321 Faculty Office 153 3 322 Faculty Office 152 3 323 Faculty Office 153 3 324 Grad Studio 317 6.6 325 Faculty Office 152 3 326 Grad Studio 317 6.6 327 Faculty Office 153 3 328 Grad Studio 317 6.6 329 Faculty Office 152 3 331 Faculty Office 153 3 332 Faculty Office 175.5 3.2 333 Closet 22 0.5 336 BCIAC 533 12.5 336B Elect Closet 31 0.6 338 CPR 593 15.1 340 CCE 555 13.1 341 CCSLC 596.5 14.8 342 Endowed Chair 164 3.6 343 Storage 75 2.3 344 E-Journal Editor 344 3.8 345 Men 225 4.8 345A Janitor 52 1.5 346 Women 269 6.2 C199D Corridor 4032 25.4 C299D Corridor 4029 57.7 124

PAGE 125

Table B-8. Natural Ventilation Airflow Ge rson Hall (output of IES simulation) Gerson Hall Room Designation Sq. Ft. avg. CFM 103 Elec 22.6 0.5 104 Janitor 32.5 0.8 105 Men 205 6.4 107 Women 221 6.9 108 Data/Comm 156 4.9 112 mechanical 516 17.2 112B Elec. 114 4 112 C Fire Pump 119 3.6 114 MACC Services 132 4.5 115 Gallery 244 8.1 116 Student Office 390 11.4 121 Medium Classroom 1327 43.9 122 Medium Classroom 1275 43.2 124 Control Room 136 4.3 125 Teaching Assistants 563.4 20 126 Large Classroom 2602 131 127 Men 264.5 8.8 128 Women 245.75 7.5 204 Janitor 37.6 0.7 205 Men 206 5.4 207 Women 209 5.3 208 Data/Comm 87.82 2.1 211 Mail 144.71 4.6 212 Clerk 85.3 2.7 213 Clerk 85.3 2.7 214 Stor/Admin Support 104.94 3.3 215 Small Conference 218.2 6 216 Director / Chair 232 7 217 Asst Dir Dept Chair 172 5.5 218 Gen Staff 111.4 3.2 219 Coord 111.4 3.2 221 Work Room 232 7 227 M Acc Reading Room 750 22.5 228 Small Classroom 808.6 24.6 229 Small Classroom 808.2 24.4 230 Break Out 106 3.3 231 Break Out 166 4.8 232 Break Out 119.4 3.5 233 Break Out 119.4 3.5 234 Break Out 119.4 3.5 235 Break Out 170.4 4.8 236 Break Out 105.12 3.3 237 Break Out 112 3.5 238 Break Out 112 3.5 240 Storage 98 2.1 303 Elec 12.4 0.2 304 Janitor 24.8 0.4 305 Men 206 6.1 307 Women 209.4 6 308 Data / Comm 88 2.4 309 Office 167 4.7 310 Office 182 5.3 125

PAGE 126

Table B-8. Continued Gerson Hall Room Designation Sq. Ft. avg. CFM 311 Office 177 5.5 312 Office 177 5.5 314 Office 177 5.5 315 Office 181.6 5.3 316 Office 223 6.6 318 PhD Office 180 5.1 319 Office 160.5 5 320 Office 166 5.2 321 Office 170.4 5.3 322 Office 160.5 5.1 324 Office 160.5 5 325 Office 160.5 5 326 Conf Rm Support 119.6 3.7 327 Large Conference Room 841.8 26.6 328 Faculty Reading / Lounge 389.2 11.8 328A Faculty Support 93 3.1 329 PhD Office 293 9.5 330 Office 152 4.8 331 Office 154.5 4.9 332 Office 154.5 4.9 333 Office 154 4.9 334 Ph D Office 365 11.5 335A Stor 39 1 336 Office 196.3 5.7 337 Office 202 5.5 338 Office 191 5.7 339 Office 184.5 5.5 340 Office 197 5.7 C199A Commons Area 3052 109.1 C199C Corridor 254 8.4 C199G Entry/Corridor 1491.64 52.2 C199H Corridor 272.77 6.8 C199J Corridor 486 16.6 C299A Corridor 567 14.9 C299B Corridor 958.32 31.7 C299C Corridor 495 14.5 C299F1 Corridor 807.32 21.6 C299F Corridor 1361 43.5 C299G Corridor 384.5 9.2 C399A Corridor 814.16 26.2 C399C Corridor 902.33 28.4 C399D Corridor 990 29.9 126

PAGE 127

127 APPENDIX C GUIDELINES FOR USING BUILDING ENERGY MODELING

PAGE 128

Table C-1. Ecotect Guidelines and Recommendations Matrix 128

PAGE 129

Table C-2. Green Building Studio Guidelines and Recommendations Matrix 129

PAGE 130

130 Table C-3. IES Guidelines and Recommendations Matrix

PAGE 131

REFERENCES ASHRAE (2004). Interpretation of ASHRAE Standard 55-2004, p. 10. ASHRAE (2007). Interpretation of ASHRAE Standard 90.1 2007. www.ashrae.org 2011. ASHRAE (2010). Interpretation of ASHRAE Standard 90.1 2010. www.ashrae.org 2011. Attia, S., Beltran, L., De Herde, A., and Hensen, J. (2009). Architect friendly: a comparison of ten different building performance simulation tools. Building Simulation, Eleventh Inter national IBPSA Conference. Glasgow, 204-211. Autodesk, Inc., Autodesk Ecotect Analysis. http://usa.autodesk.com/ adsk/servlet/pc/index? siteID=123112&id=12607162 2011. Autodesk, Inc., Autodesk Green Building Studio. http://usa.autodesk.com/adsk/serv let/pc/index?id=11179508&siteID= 123112 2011. Azhar, S., Brown, J., & Fa rooqui, R. (2009). BIM-based sustainability analysis: An evaluation of building per formance analysis software. Proceedings of the 45th ASC Annual Conference, Gainesville, Florida, April 1-4, 2009. Azhar, S., Carlton, W.A., Olsen, D., and Ahmad, I. (2011). Building information modeling for sustainable design and LEED rating analysis. Automation in Construction 20 (2011), 217-224. Bentley Systems, Inc., Bent ley Hevacomp Simulator. http://www.bentley.com/en us/ products/hevacomp+dynamic+simulation/. 2011. Bentely Systems, Inc., Bentley Tas Simulator http://www.bentley.com/en 141 US/Products/Bentley+Tas/. 2011. Bringezu, S. (2002). Chapt er 8: Construction Ecology and Metabolism. Construction Ecology: Nature as the Basis for Green Buildings. Spon Press, New York. Crawley, D.B., Hand, J.W., Kummert, M., and Griffith, B. T. (2008). Contrasting the capabilities of building energy per formance simulation programs. Building and Environment 43 (2008), 661-673. Eastman, C.M., P. Teicholz, R. Sacks, and K. Liston. 2008. BIM Handbook: a guide to building information modeling for owner s, managers, designers, engineers, and contractors, John Wiley and Sons, Inc., Hoboken. 131

PAGE 132

Integrated Environmen tal Solutions (2010). IES Capabilities Matrix VE-Ware, VEToolkits, VE-Gaia, and VE-Pro http://www.iesve.com/s oftware/flyers/ies_ capabilities_matrix_v6.2_nov10__globa l_.pdf. 2011. Khemlani, L. (2004). T he IFC Building Model. AECbytes, March 30, 2004. http://www.aecbytes.com/feature/2004/IFCmodel.html 2011. Krygiel, E., and Nies, B. (2008). Green BIM: Successful Sustainable Design with Building Information Modeling Wiley Technoloy Pub., Indianapolis. Kwok, S., and Lee, E. (2011). A study of the impor tance of occupancy to cooling load in prediction by intelligent approach. Energy Conversion and Management, 52 (2011), 2555-2564. Platt, G., Li, J., Li, R., Poulton, G., James, G., and Wall, J. (2010). Adaptive 142 HVAC Zone modeling for sustainable buildings. Energy and Buildings; (42:4); pp. 412421. Schlueter, A., and Thesseling, F., (2009) Building information model based energy/exergy performance assessm ent in early design stages. Automation in Construction 18 (2009), 153-163. Smith, A.; Fumo, N.; Luck, R.; Mago, P.J. (2011). Robus tness of a Methodology for Estimating Hourly Energy Consumption of Buildings Using Monthly Utility Bills. Energy and Buildings ; (43:4); pp. 779-786. Sozer, H. (2010). Improving energy efficien cy through the design of building envelope. Building and Environment, 54 (2010), 2581-2593. Thoo, S. (2008). Graphisoft Ec odesigner (Product Review). AECbytes, February, 11, 2010. http://www.aecbytes.com/review/2010/EcoDesigner.html 2011. Turner, C., and Frankel, M. (2008). Energy Performance of LEED for New Construction Buildings New Buildings Institute, USGBC. Vancouver, WA. U.S. Green Building Council (2007). Building Design Leaders Collaborating on CarbonNeutral Buildings by 2030 Goal to Meet Specific Energy Reduction Targets. http://www.usgbc.org/News/Pres sReleaseDetails.aspx?ID=3124 2011. U.S. General Services Administration, Public Buildings Se rvice (2009). GSA BIM Guide for Energy Performance. GSA BIM Guide Series 05. U.S. Department of Energy. Building Energy Software Tools Directory .143 http://apps1.eere.energy.gov/buildings/t ools_directory/subjects.cfm/pagename=s ubjects/pagename_menu=whole_building_analysis/pagenam e_submenu=energy _simulation 2011. 132

PAGE 133

U.S Deparment of Energy, Weather file database http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data.cfm 2011. U.S Deparment of Energy, Gainesville Weather file location database ( http://apps1.eere.energy.gov/buildings/ energyplus/cfm/weather_data3.cfm/regio n=4_north_and_central_amer ica_wmo_region_4/country =1_usa/cname=USA#in structions ). 2011. Velds, M., and Christoffersen, J. (2001). Monitoring Procedur es for the Assessment of Daylighting Performance of Buildings IEA SHC Task 21/ ECBCS Annex 29. International Energy Agency. Younker, DL. 2003. Value Engineering Marcel Dekker, New York. 133

PAGE 134

134 BIOGRAPHICAL SKETCH Thomas (TJ) Reeves received his Bac helor of Architecture at Syracuse University School of Architecture in 2009 and Master of Science in Building Construction with a concentration in sustainability at the Un iversity of Florida M.E. Rinker, Sr. School of Building Construction in 2012. He is a co-founder of the design firm Lusona Design with built work in the Philippines, and a project underway in Los Angeles. His interests in art, science and culture have led him to a passion for the built environment. In seeking a masters degree in building construction in addition to a bachelors degree in architecture, he seeks to bridge the divide between designer and builder. Whether as designer, builder, or rese archer, he sees the pr oduction of the built environment simply as craft.