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Thermal and daylighting performance of automated split-controlled blinds

Permanent Link: http://ufdc.ufl.edu/UFE0041261/00001

Material Information

Title: Thermal and daylighting performance of automated split-controlled blinds
Physical Description: 1 online resource (94 p.)
Language: english
Creator: Golasangimath, Deepak
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: automated, blinds, conventional, daylighting, energy, energyplus, radiance, software, split, validation, venetian
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: This research dealt with the study of thermal and daylighting performance of the proposed split-controlled blind system, as compared to a conventional Venetian blind system commonly used in commercial offices. Conventional blinds are most commonly used for the purpose of blocking direct sunlight and preventing glare to the occupants. Conventional blinds are not efficiently used to maximize daylight once the direct light is no longer available. Also, keeping the blinds open for longer periods with direct light coming inside the building increases the cooling loads for the building. The proposed split-controlled blinds consist of multiple sections of blinds, each section with a separate blind angle to allow sufficient daylight inside the building, prevent glare to the occupants and reduce cooling loads for the office building. The case study for this research comprised of a simple office room with a south facing window and interior blinds system installed. EnergyPlus, thermal and energy simulation software and Radiance, daylighting analysis software were used as tools to simulate the office room. Seven static blind angle settings in conventional blinds as a base case and 29 split blind angle settings as a prototype case were simulated by EnergyPlus (EP) and Radiance, to study the thermal and daylighting performance of the proposed split-controlled blind system. Conservative energy savings were observed for the split-blinds system for the three days of energy simulations. Within the comfort range between 500 lux and 2000 lux, higher average illuminance values with less daylighting fluctuation were observed during the course of the entire year as a result of using the split-blinds system. No observable difference in Useful Daylight Illuminances (UDI) performance was observed between the split-controlled blind system and conventional blind system.
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 Deepak Golasangimath.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2009.
Local: Adviser: Olbina, Svetlana.
Local: Co-adviser: Issa, R. Raymond.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041261:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041261/00001

Material Information

Title: Thermal and daylighting performance of automated split-controlled blinds
Physical Description: 1 online resource (94 p.)
Language: english
Creator: Golasangimath, Deepak
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: automated, blinds, conventional, daylighting, energy, energyplus, radiance, software, split, validation, venetian
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: This research dealt with the study of thermal and daylighting performance of the proposed split-controlled blind system, as compared to a conventional Venetian blind system commonly used in commercial offices. Conventional blinds are most commonly used for the purpose of blocking direct sunlight and preventing glare to the occupants. Conventional blinds are not efficiently used to maximize daylight once the direct light is no longer available. Also, keeping the blinds open for longer periods with direct light coming inside the building increases the cooling loads for the building. The proposed split-controlled blinds consist of multiple sections of blinds, each section with a separate blind angle to allow sufficient daylight inside the building, prevent glare to the occupants and reduce cooling loads for the office building. The case study for this research comprised of a simple office room with a south facing window and interior blinds system installed. EnergyPlus, thermal and energy simulation software and Radiance, daylighting analysis software were used as tools to simulate the office room. Seven static blind angle settings in conventional blinds as a base case and 29 split blind angle settings as a prototype case were simulated by EnergyPlus (EP) and Radiance, to study the thermal and daylighting performance of the proposed split-controlled blind system. Conservative energy savings were observed for the split-blinds system for the three days of energy simulations. Within the comfort range between 500 lux and 2000 lux, higher average illuminance values with less daylighting fluctuation were observed during the course of the entire year as a result of using the split-blinds system. No observable difference in Useful Daylight Illuminances (UDI) performance was observed between the split-controlled blind system and conventional blind system.
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 Deepak Golasangimath.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2009.
Local: Adviser: Olbina, Svetlana.
Local: Co-adviser: Issa, R. Raymond.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041261:00001


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1 THERMAL AND DAYLIGHTING PERFORMANCE OF AUTOMATED SPLIT CONTROLLED BLINDS By DEEPAK V. GOLASANGIMATH 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 2009

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2 2009 Deepak V. Golasangimath

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3 To my family and friends, for all the support and guidance they prov ided throughout my student life

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4 ACKNOWLEDGMENTS I would like to thank Dr. Svetlana Olbina for her constant support and motivation throughout my course work and research. I would also like to thank Dr. Raymond R. Issa for all his valuable inputs into this research, without his support thi s research could not have been completed. I express my gratitude to Mr. Richard Kelley, Senior Systems Programmer, for his tireless effort to make this research possible by running the simulations in the computer lab. Also, I would like to thank committee member Dr. Robert J. Ries for his valuable inputs on the research. I would also like to thank my parents and my sister for supporting me throughout my education life and to all my friends for all the ways in which they supported me throughout my research.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 8 ABSTRACT ................................................................................................................... 10 CHAPTER 1 INTRODUCTION .................................................................................................... 12 Problem Statement ................................................................................................. 12 Research Objectives ............................................................................................... 13 Limitations of Research .......................................................................................... 14 2 LITERATURE REVIEW .......................................................................................... 16 Manually Controlled Blinds ..................................................................................... 16 Automated blinds .................................................................................................... 17 Energy Analysis ...................................................................................................... 18 Blinds Control Strategies ........................................................................................ 19 Energy Plus and Radiance ...................................................................................... 21 EnergyPlus (EP) ............................................................................................... 21 Radiance .......................................................................................................... 22 3 METHODOLOGY ................................................................................................... 24 Introduction ............................................................................................................. 24 Description of Case Study ...................................................................................... 24 Location and Weather Data: ............................................................................. 26 HVAC ............................................................................................................... 26 Simulations ............................................................................................................. 26 EnergyPlus Inputs ............................................................................................ 28 Selecting the walls and windows ................................................................ 28 Material Regular ......................................................................................... 29 Design Day ................................................................................................ 29 Construction ............................................................................................... 29 Material: Blinds .......................................................................................... 30 Surface(s) .................................................................................................. 30 Lights ......................................................................................................... 30 Schedule .................................................................................................... 31 Daylighting: Detailed .................................................................................. 31 Lighting Control Type ................................................................................. 32

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6 Selecting Input and Weather Files ............................................................. 33 Radiance .......................................................................................................... 34 Quality settings ................................................................................................. 37 Ambient bounces ( ab): .............................................................................. 38 Ambient value ( av): ................................................................................... 38 Ambient Divisions ( ad) .............................................................................. 38 4 RESULTS AND ANALYSIS .................................................................................... 40 Introduction ............................................................................................................. 40 EnergyPlus Results A nalysis .................................................................................. 40 Daylighting Analysis ................................................................................................ 42 Choosing Blind Angle ....................................................................................... 48 Radiance Results Analysis ............................................................................... 49 Automation Model ................................................................................................... 65 5 CONCLUSIONS AND RECOMMENDATIONS ....................................................... 67 Conclusions ............................................................................................................ 67 Recommendations .................................................................................................. 68 APPENDIX A ENERGYPLUS SPECIFICATIONS ........................................................................ 69 B RADIANCE GENERATORS ................................................................................... 72 C RADIANCE SCRIPT ............................................................................................... 73 D ENERGYPLUS INPUTS ......................................................................................... 75 LIST OF REFERENCES ............................................................................................... 92 BIOGRAPHICAL SKETCH ............................................................................................ 94

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7 LIST OF TABLES Table page 3 1 Example of a Stepped Lighting Control System with Three Steps ...................... 33 4 1 Energy consumption for lighting (W), HVAC (W) and Total Electric Demand (W) for March 21, June 21 and December 21 .................................................... 43 4 2 EnergyPlus Comparison Results ........................................................................ 44 4 3 UDI values for Sensor Point 1 ............................................................................ 50 4 4 UDI values for Sensor Point 2 ............................................................................ 51 4 5 UDI values for Sensor Point 3 ............................................................................ 52 4 6 UDI values for Sensor Point 4 ............................................................................ 53

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8 LIST OF FIGURES Figure page 1 1 A three dimensional view of a Google SketchUp image for the proposed split controll ed blind system. The split blinds system is divided into three sections: Top section, Middle section, and Bottom section ................................................ 14 3 1 Three dimensional Google SketchUp view of the office room. ........................... 25 3 2 Top view the office building with the two sensor points positioned on two office desks. ........................................................................................................ 26 3 3 The input for EnergyPlus was done through the IDF Editor interface. ................ 28 3 4 The IDF interface for Design Day Input. ............................................................. 29 3 5 Screen shot showing all the necessary variables for the object surface heat transfer. .............................................................................................................. 31 3 6 The fi rst drop down menu is used for selecting the input file (IDF editor file) created using IDF Editor. .................................................................................... 33 3 7 The second dropdown menu is used for selecting the weather file. .................. 34 3 8 Fish eye view of a Radiance rendered image of the office room. ....................... 36 3 9 Close up view of the office room. ....................................................................... 37 4 1 Energy consumption differences between split controlled blinds and conventional blinds for March 21st ..................................................................... 45 4 2 Energy consumption differences between split controlled blinds and conventional blinds for June 21st. ...................................................................... 46 4 3 Energy consumption differences between split controlled blinds and conventional blinds for December 21 ................................................................. 47 4 4 UDI comparison for Sensor Point 1(SP1). .......................................................... 55 4 5 UDI comparison for Sensor Point 2 (SP2). ......................................................... 56 4 6 UDI comparison for Sensor Point 3 (SP3). ......................................................... 57 4 7 UDI comparison for Sensor Point 4 (SP4). ......................................................... 58 4 8 Frequency distribution of illuminance values from split controlled blinds for sensor point 1. .................................................................................................... 61

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9 4 9 Frequency dist ribution of illuminance values from conventional blinds for the first sensor point. ................................................................................................ 61 4 10 Illuminance values for all the split blind angles, at the first sensor point for January 1 at 8 a.m. ............................................................................................. 62 4 11 Average illuminance distribution at the four sensor points. ................................. 62 4 12 Yearly illuminance values at the first sensor point. ............................................. 63 4 13 Yearly illuminance values at the second sensor point. ....................................... 63 4 14 Yearly illuminance values at the first sensor point. ............................................. 64 4 15 Yearly illuminance values at the first sensor point. ............................................. 64 4 16 Automation algorithm for split controll ed blinds. ................................................. 66

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10 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 THERMAL AND DAYLIGHTING PERFORMANCE OF AUTOMATED SPLIT CONTROLLED BLINDS By Deepak V. Golasangimath December 2009 Chair: Svetlana Olbina Cochair: R Raymond Issa Major: Building C onstruction This research dealt with the study of thermal and daylighting performance of the proposed split controlled blind system, as compared to a conventional Venetian blind system commonly used in commercial offices. Conventional blinds are most commonly used for the purpose of blocking direct sunlight and preventing glare to the occupants. Conventional blinds are not efficiently used to maximize daylight once the direct light is no longer available. Also, keeping the blinds open for longer periods with direct light coming inside the building increases the c ooling loads for the building. The proposed split controlled blinds consist of multiple sect ions of blinds, each section with a separate blind angle to allow sufficient daylight inside the building, prevent glare to the occupants and reduce cooling loads for the office building. The case study for this research comprised of a simple office room w ith a south facing window and interior blinds system installed. EnergyPlus thermal and energy simulation software and Radiance, daylighting analysis software were used as tools to simulate the office room. Seven static blind angle settings in conventional blinds as a base case and 29 split blind angle settings as a prototype case were simulated by Energy Plus (EP) and Radiance, to study

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11 the thermal and daylighting performance of the proposed split controlled blind system Conservative energy savings were observed for the split blind s system for the three days of energy simulations. Within the comfort range between 500 lux and 2000 lux, higher average illuminance values with less daylighting fluctuation were observed during the course of the entire year as a result of using the split blind s system. No observable difference in Useful Daylight Illuminances (UDI) performance was observed between the split controlled blind system and conventional blind system.

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12 CHAPTER 1 INTRODUCTION Blinds are the most commonly used shading devices in commercial and residential buildings. Blinds consist of slats of wood, fabric or metal which are made to overlap one another to cover the window. They are operated by rotating the slats from an open position to a closed position by making them overlap each other. Venetian blinds consist of horizontal slats connected by cloth stripe s called tapes or chords which help in rotating all the slats in unison from 0 to 180 degrees. In general, split blinds were defined as conventional V enetian blinds divided into multiple sections. Each section of the split controlled blinds would perform a specific function. In this research, split blinds wer e divided into three sections. Each section of the split controlled blind system had different slat angle covering different sections of the window. Figure 11 shows split controlled blind system in which each section performs different functions: The top section performs the function of transmission of low angled daylight so that light penetrates into the remote part of the room. The middle section performs the function of allowing all the daylight to come in, and at the same time preventing direct sunlight, which causes glare, thus creating discomfort for occupants. The lower section performs the function of not allowing too much heat to come inside and thus prevents overheating of the room. Problem Statement Buildings in United States account for 40% of the total energy consumption. Substantial research has been carried out in the field of sustai nable construction to reduce the amount of energy consumed in buildings Research studies related to

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13 advanced glazings and daylighting systems have been made to maximize daylighting and reduce glare inside the buildings ( Tzempeliko s et al.2007 ). Venetian blinds are one of the most common shading devices used in most commerc ial and residential buildings. Conventional Venetian blinds are most commonly used to block direct sunlight and prevent glare. Conventional Venetian blinds are rarely used to maximize d aylight penetration into the building. Significant electric lighting energy savings can be obtained by proper use of daylighting in buildings ( Lee et al. 1998a). In regions dominated by warm weather, keeping blinds open for longer periods allows excess heat inside the building and thus increase s the cooling load. A more efficient blind system which prevents glare to the occupants and reduces energy consumption in buildings is a necessity. The proposed split controlled blind system performs the function of providing better daylighting into the building Split controlled blind system also reduces the Heating Ventilation and Air Conditioning (HVAC) load by preventing excess heat entering the room and allow ing sufficient daylighting, w hich, in turn, reduces the use of electric lighting. Research Objectives The objective of this research was to study thermal and daylighting performance of a new shading device system, the split controlled blind system The thermal and daylighting performances of split controlled blinds were compared to the performance of conventional blinds This research determined energy savings and useful daylight illuminance as a result of using the split controlled blinds. Thus this research tried to answer the following questions: Does split controlled blind system give higher energy savings and perform better thermally as compared to the conventional blind system ?

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14 Does split controlled blind system provide higher daylighting levels? Figure 11. A threed imensional view of a Google SketchUp image for the proposed split controlled blind system The split blinds system is divided into three sections: Top section, Middle section, and Bottom section Limitations of Research The test facility for this experiment was a small office room 3 m x 3.5 m x 5 m in size located in Gainesville, Florida. This research was validated by testing the proposed split controlled blind system by using two simulation software tools: EnergyPlus and Radiance. However, it should be noted that this experiment did not take into consideration other possible external conditions such as a presence of trees and other sources of shade. The performance of proposed split controlled blinds is yet to be tested in complex building systems.

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15 The abi lity of the software tools used to accurately simulate the desired conditions is a factor to be considered while validating these theoretical results. Radiance is considered a highly accurate software program for simulating lighting conditions for threedi mensional models of buildings, but Radiance requires a high amount of computing power, as well as a thorough knowledge of daylighting and optics. EnergyPlus (EP) is considered to be one of the best software engines for energy simulations of buildings, but it is not the best software to be considered for daylighting analysis.

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16 CHAPTER 2 LITERATURE REVIEW The function of a shading device is to prevent glare caused by direct sunlight as well as to prevent excess heat entering into the building. Venetian blinds are one of the most commonly used shading devices in office buildings. Heating/cooling energy consumption, along with the thermal and visual comfort is highly affected by the type of shading device used ( Tzempeliko s et al. 2007). Manually Controlled B linds Manually controlled blinds perform the function of blocking excess direct light coming into the building and preventing glare. Excess heat entering through the fenestration leads to an inc rease in cooling load for the building (Tzempelikos et al. 2007). The process of manually operating blinds by the occupant is often found to be cumbersome. Most of the time the user tend s to leave the blinds open all day, which leads to excessive heat comi ng into the building thus increasing the cooling load for the building. Just the presence of blinds can affect the illuminance level inside the room, w hen compared with blinds totally retracted. T he horizontal blind slats reduced the illuminance at a photosensor on the ceiling by 30% to 50% for about eight hours between 6 a.m. t o 6 p.m. in September and May which increased the lighting consumption of the test room by approximately 30% (Galasi u et al. 200 4 ) Positioning the blinds with the slats at a 45 upward angle reduced the total illuminance at the ceiling on average by 45% to 60% in both September and May for about 8 to 10 hours a day. Results showed that blinds positioned with their slats at a 45

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17 downward angle reduced the total illuminance at the ceiling photosensor by 40% to 45% for 7 hours in the spring, and 10 hours in the fall ( Galasiu et al. 200 4 ) Automated blinds In the case of automated blinds the slat angle of the blinds is controlled by a computer program. Dynamic blinds are controlled automatically with the help of a motor or mechanized system to make the blinds system perform a particular task such as block direct sunlight or to maintain a particular illuminance level inside the room. The category of dynamic window technologies enc ompasses numerous conventional components such as motorized louvers, venetian blinds, and shades, as well as more advanced glazing systems such as switchable electrochromics, photochromics, thermochromics, polymer dispersed liquid crystal glazings, and electrically heated glazings ( Lee et al. 1998 b ) However, the potential for a dynamic operation of shading devices through a building automation system is generally neglected. The impact of shading design and control on building performance is not taken into account at the design stage, although an optimum cooling and lighting energy balance between fenestration and lighting may be identi ( Tzempelikos et al. 2006). As a result of the dynamic systems control over solar heat gain, reductions i n cooling load were observed. Also lighting energy savings were observed as a result of dynamic systems ability to control the illuminance levels inside the room. Dynamic blinds were found to perform better than the static blind system in terms of cooling load reductions and peak load reduction (Lee et al 1998b ) Total annual energy savings of 1626% were attained with the automated blind compared to an unshaded low E spectrally selective window system with the same daylighting controls in Los Angeles, Ca lifornia (Lee et al. 1998 a )

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18 Field data indicated that an automated interior blind with spectrally selective glazing and a less than optimal control algorithm was more than twice as effective at reducing peak solar gains under clear sky conditions as a static unshaded bronze glazing with the same daylighting control system, while providing the same level of useful daylight ( Lee et al. 1998b ) Energy A nalysis One of the main purposes of using Venetian blinds as a shading device is to avoid the flow of excess heat coming into the room, thus increasing the cooling load for the building. Even in heating dominated climates, cooling is important for perimeter spaces with high solar gains. Shading provision is necessary; the properties and control of shading have to be taken into account from the early design stage, since they have a significant impact on peak thermal loads, energy consumption for heating, cooling and lighting, as well as on human comfort ( Tzempelikos et al. 2006). On e quarter of total energy usage in the commercial sector is accounted by lighting energy consumption in buildings, followed by heating, cooling, office equipment and water heating ( Koomey et al. 2001). In one comparative study by Lee et al ( 1998 a) the bas e case was defined as automated blinds with static positions, divided into two cases, one with daylighting control and one without daylighting control. The prototype case consisted of automated blinds activated every 30 seconds to maintain illuminance of 5 40700 lux. The same ballast system was used for lighting control in both cases. Average cooling reductions of 6% to 15% (for 45 (for 0 Average peak load reductions were 6% to 15% by dynamic blinds as compared to static blinds (for 45 blinds (for 0 static angle). For the base case without daylighting controls, daily lighting ener gy savings by dynamic blinds were 22% to 86% for any static blind angle. Cooling

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19 load reductions for dynamic blinds were on the order of 28 5% as compared to the static blinds for 0 blind angle for clear days in July. In the case of the static system a n imbalance occurred between cooling load reductions and lighting energy savings for closed and horizontal position of the blinds. This balance was achieved in case of the dynamic system. The dynamic system always blocked direct sunlight and provided view for a maximum of 50% of the day throughout the year thus reducing the occu pants discomfort considerably (Lee et al 1998a ) Blinds C ontrol Strategies For climates with moderate daylight availability and for a buil ding type that is cooling load dominated, dynamic window technologies can be coupled with daylighting controls to actively optimize daylight and its respective solar heat gains at the perimet er zone of commercial buildings (Lee et al. 1998). If automated blinds were interconnected to a lighting system, such that the amount of electric lighting used was linked with the daylight available inside the building, sufficient energy savings could be observed. A variety of control alternatives have been suggested to reduce lighting energy consump tion. D aylight dimming control systems save lighting energy most effectively since they use daylight as an alternative light source (Kim and Kim, 2007) The dimming system had the potential to save between 50% and 60% lighting energy over the 12hour peri od considered (6 a.m. to 6 p.m. ) compared to the electric lights being fully on during the same time interval (Gala si u et al. 2004). A pproximately 20% to 40% of lighting energy consumption in buildings can be reduced by using daylight dimming control syst ems ( Kim et al. 2007 ). However, daylight dimming systems have not been widely applied to buildings due to the visual problems associated with fluctuating electric light outputs under varying sky and cloud conditions

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20 Use of off the shelf technology can be made to promote efficient window systems. For example, (Lee et al. 2004) attempted to dev elop a low cost networking system for dynamic window, lighting and sensors devices. Lawrence Berkeley National Laboratory (LBNL) developed a building communication net work, known as the I ntegral B uilding E nvironment C ommunication S ystem (IBECS) T his system was found to be cheaper due to a low per point networking cost. This cost effective network interface for AC/DC motorized shading and a switchable electrochromic window system was developed by using a microlan bridge to couple the various devices and sensors in a building with the existing Ethernet Network. The IBECS achieved significant reductions in per point networking costs. By making some modifications to the interface between the motor and the shading devices, such a system can also be applied to all types of motorized window shading systems (Lee et al. 2004) In another experiment by Galasiu et al. (2004), 15 photometric sensors were positioned for measuring exterior as well as interior illuminance levels ; three of them were at the center of the ceiling. These three sensors were set to have partially shielded, fully shielded and no shield ed conditions. The sky conditions were classi fied into clear sky conditions and partly cloudy sky conditions. Three blind conditions : no blind condition horizontal blinds and blinds at a 35 T he shielding conditions used in the photo sensors significantly controlled th e daylight fluctuations caused by the partly cloudy sky conditions. The slat angle of the V enetian blind used was not a significant factor in controlling or determining the interior illuminance level The altitude and azimuth angles were important factors under partly cloudy sky conditions for caus ing the change in fluctuations (Gala si u et al. 2004)

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21 EnergyPlus and Radiance EnergyPlus (EP ) EnergyPlus is a building energy simulation program. Various components of building dynamics such as heating, cooling, lighting, ventilation, and energy flows can be modeled in EnergyPlus EnergyPlus is a standalone simulation program without a graphical user interface (GUI). A number of GUI s are being developed to make the software user friendly. Some of the commonly use d GUIs are Open Studio, EnergyPlugged, DesignBuilder, and ECOTECT. Building Loads Analysis and System Thermodynamics (BLAST) and DOE 2 are computer programs used to perform the heating and cooling load calculations for buildings. Their origins date from t he early 1970s. They are primarily used to investigate the energy performance of new as well as retrofit building design options. They possess different capabilities such as peak load calculation and annual energy performance of building facilities of any size and type. EnergyPlus was built on the platform of BLAST and DOE 2. EnergyPlus possesses many of their capabilities with many innovative simulation capabilities such as time step less than an hour, modular systems, and plant integration with heat balancebased simulation, Multizone airflow, thermal comfort, water use and natural ventilation. The disadvantages of EnergyPlus include: EnergyPlus does not have a Graphical User Interface (GUI). Though a number of third party interfaces are being developed, EP is a simulation engine. EP is not a life cycle assessment program. EP cannot be used as a L ife C ycle C osting (LCC) tool.

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22 EnergyPlus also cannot be considered as a replacement for an architect or a design engineer. EP inputs and outputs have to b e monitored by an engineer or an architect to co rrectly interpret the results ( EnergyPlus Documentation 2008). Radiance Radiance is software tool to simulate various lighting conditions. Radiance makes visualization of lighting effortless in a virtual envi ronment because of the endless possibilities that arise from its 50 or so tools. These tools can be used to produce realistic and natural renderings of complex building systems with complex lightings. Radiance synthesizes images from threedimensional geo metric models. The model consists of the description of the surfaces shape, size, location, and composition. Generally simple surfaces such as polygons, spheres and cones can be directly modeled in Radiance. A number of other generator programs can be used to produce more complex shapes such as boxes prism s, and surfaces of revolution. More complex structures consisting of thousands of surfaces can be produced separately by Computer Aided Design (CAD) programs (Larson 1991). Radiance is UNIX based software, thus familiarity with the UNIX operating system is necessary in order to use Radiance. Radiance perform s five main functions : Produces shapes of objects in an environment by entering and compiling information by various methods described in Larson and Shakespeares book Rendering with Radiance. Characterizes the lights interaction with the surfaces using various mathematical models described in the book. Simulates and renders lighting, certain techniques can calculate the propagation of light in an environment, as well as the nature of the values computed. Analyzes and manipulates images, the image processing and conversion.

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23 Creates interactions between automation of rendering and analysis processes and facilitates link s to other systems and computing environments (Larson and Shakespeare 1998)

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24 CHAPTER 3 METHODOLOGY Introduction This research evaluated the performance of spl it controlled blind system by comparing its thermal and daylighting performance with conventional Venetian blind system. Two building simulation tools, EnergyPlus (EP) and Radiance, were used to validate the proposed split controlled blind system. In Energ yPlus, simulations were performed for three days: March 21, June 21, and December 21. Daylighting assessment was done using the software, Radiance. Simulations were performed for every occupied hour for the entire year to obtain illuminance values for fou r sensor points placed one meter from each other. The first sensor point was placed one meter from the window. Description of Case Study The case study involved a small 3 m x 3.5 m x 5 m office room in Gainesville, Florida. The office room was fitted wit h a 1.83 m x 1.83 m (6 x 6) window (U factor=2.69, visible transmittance=0.744) on the south side. The window was glazed with double insulated glass that consisted of: a 6 mm interior glass (Low emittance glass (Low E), conductivity=0.6 W/m K, thickness= 0.006 m, solar transmittance=0.6, solar reflectance on front side=0.22, solar reflectance on back side=0.17), 13 mm air space, and a 6 mm Low E exterior glass. No frame was used for the window. The wall surface consisted of three layers: concrete with steel connectors on the outside (thickness=0.15 m, conductivity=1.078 W/m k, density=2185.44 kg/ m3, specific heat=1173 J/kg K), an insulation in the middle (thickness=0.1 m, conductivity=0.499 W/m K, density=1552 kg/ m3, specific heat=880 J/kg k), and concrete with steel

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25 connectors on the inside (thickness=0.1 m, conductivity=1.329 W/m k, density=2213 kg/ m3, specific heat=964 J/kg K). Blinds with high reflective slats were used. Slat width was 0.025 m; slat separation was 0.0187 m and slat conductivity of 0.9 W/m K. EnergyPlus simulations were performed for three days of the year, March 21 (maximum dry bulb temperature equal to 31.7 C, barometric pressure equal to 101217, wind speed equal to 12.9 m/s), June 21 (maximum dry bulb temperature equal to 34.4 C, b arometric pressure equal to 101217, wind speed equal to 9.3 m/s) and December 21 (maximum dry bulb temperature equal to 27.2 C, barometric pressure equal to 101217, wind speed equal to 8.8 m/s). Figure 31 shows the threedimensional model of the office room described in the case study. This Google SketchUp model was imported into EP using the Plug in, OpenStudio. Figure 31. Three dimensional Google SketchU p view of the o ffice room Figure 32 shows the top view of the office room. The two sensor points used in EP were collinearly placed at distance of 0.75 m and 3.5 m with the windows midpoint at a height of 0.76 m above the ground level.

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26 Figure 32 Top view the office building with th e two sensor points positioned on two office desks. Location and W eather D ata : The weather file for the G ainesville region was obtained from the U S D epartment of E nergy (DOE) B uilding T echnologies (BT) Program website. Elevation of the building is 41 m above sea level and standard pressure was 100833 Pa. (EnergyPlus Weather file, 2008). HVAC The HVAC system used for the EnergyPlus simulation was Packaged Terminal: Air Conditioner ( PTAC). The PTAC consists of an outside air mixer, direct Expansion (DX) cooling coil, and a heating coil which may be run by gas, electricity, hot water steam and a supply air fan (EnergyPlus Documentation, 2008). Simulations Simulations were conducted for seven different static slat angles (0 15 30 45 60 75 and 90 ) in th e case of conventional blinds. In the case of split blinds the

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27 following 29 set s of split angles were tested: 0 0 0 0 0 8 0 0 0 45 1 0 0 0 1 0 0 45 1 0 0 8 0 37 45 45 37 45 8 0 37 45 0 37 45 1 0 37 0 0 37 0 8 0 37 0 45 0 45 0 0 45 0 0 45 45 0 45 8 0 0 8 0 0 0 8 0 45 0 8 0 8 0 1 0 45 0 1 0 45 45 1 0 45 8 0 1 0 8 0 0 1 0 8 0 45 1 0 8 0 8 0 37 8 0 8 0 37 8 0 45 and 37 8 0 0 For split blinds, the angles are specified in the order from the top section to the bottom section. For example, 1 0 0 45 means the top b linds would be 10 closed downwards the middle angle would be totally open at 0 angles and the bottom section would have an angle of 45 closed downwards The 37 slat angle indicated the slat angle rotation of 37 inwards facing the room allowing for more daylight penetration at the backend of the room. The o ffice room had two sensor points, e ach placed at a height of 0 .76 m above the ground level. The first sensor point was placed 0.75 m from the window and the second sensor point was placed 3.5 m from the window. The following variables were measured at the two sensor point s: Average Illuminance (lux) Glare Index, Average hourly lighting load (W), Average hourly HVAC load (W), Average Total Energy Consumption (W) for three days of the year : March 21, June 21, and December 21 for every occupied hour, from 8 a.m. to 5 p.m. June 21 is one of two solstices. On t his day the rays of the sun directly strike the Tropic of Cancer at 2330' north latitude On June 21 the summer solstice marks the beginning of summer in the N orthern H emisphere and subsequently marks winter in the S outhern H emisphere. On December 21, the winter solstice mark s the beginning of winter in the Northern H emisphere and marks summer in the S outhern H emisphere.

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28 Therefore Ma rch 21, June 21, and December 21 become important day s for performing simulations. EnergyPlus Inputs The IDF editor is the interface used by the user to define all the parameters required to run the simulation. The threedimentional model created in Google SketchUp was imported in EnergyPlus using IDF Editor. The graphical interface for IDF Editor is shown in Figure 33. The IDF Editor lists all the input parameters as objects to be defined by the user. The value to be inputted is defined briefly in the box named comments section on the right side of the checklist. The important parameters that need to be de fined before running the simulations are as follows: Figure 33. The input for EnergyPlus was done through the IDF Editor interface. S electing the walls and windows The parameter surface construction was used to describe the physical properties and the configuration for the building envelope and the interior elements.

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29 Material Regular This parameter was used to define the four main thermal properties of the material: thickness, conductivity, density and specific heat A ppendix A contains all the valu es assigned for each of the required variables for the definition of the parameter. Design D ay Figure 34 shows the object Design day, this parameter defines all the variables for the day required to run the simulation. The day of the month and month of the year for which the simulations are to be run are defined here. Others prominent variables such as maximum D rybulb temperature and W indspeed, are defined here Figure 34. T he IDF interface for Design D ay Input. Construction Once all the m aterials are defined in the parameter Material Regular a ll the elements of the building such as walls, floors, windows, and doors are built in this

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30 parameter from the materials defined. From the most exterior layers, up to 10 layers can be specified. Material: B linds This parameter defines all the dimensions of the blinds as well as material properties of the blinds. B linds can be placed either inside of the window, on the outside of the window or between two layers of glass. For this case study, the blinds ar e placed inside the window. The blinds are assumed to cover all the glazed part of the window. Surface(s) The parameter Surface heat transfer was used to define all the geometric specifications for all the building elements, walls, window, slab, and roof. This parameter not only defines the important elements of the building but also the interaction between all the building surfaces. Figure 35 shows the parameter surface h eat t ransfer selected in the Thermal Zone Description Geometry section. This paramet er consi sts of the variables, Surface Type, Sun Exposure, Wind Exposure, and the coordinates for each surface v ertex of the surface selected. Lights The parameter L ights give s the option to define the type and intensity of the interior lights used. This c ase study used 256 W of interior lighting for the office building.

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31 Figure 35 Screen shot showing all the necessary variables for the object s urface h eat transfer. Schedule Electrical equipment, such as Heating Ventilation and Air Conditioning (HVAC), and lights can be set on schedule by using the parameter schedule compact. The parameter schedule compact defines the time period of the day for which the electric appliance was in use. Daylighting: Detailed The parameter Daylighting: Detailed defines all the necessary variables required to calculate illuminance levels at the required sensor points. Many variables, such as sky condition, sun position, calculation point (sensor point), location of the sensor point, and transmittance value of the windows, were defined here. The dimming strategy used to control lighting depended on the illuminance set point that was chosen, the fraction of

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32 the zone that the set point controls, and the type of lighting control used (EnergyPlus Documentation, Version 2.2, 2008). Lighting Control Type EnergyPlus has two types of lighting control mechanisms. In the first mechanism, the overhead lights dim continuously and linearly from maximum electric power and maximum light output to minimum electric power and minimum light output as the daylight illuminance increases inside the room. The lights stay on at the minimum point with further increase in the daylight illuminance. In the second mechanism, known as stepped control of lighting, the electric power input and light output vary in discrete, equally spaced steps (EnergyPlus Documentation, 2008). For this case study, the stepped control of lighting was used. For the two sensor points in the model, the illuminance setpoint was 500 lux. The stepped control of light works in the following manner: For an illuminance value of less than onethird of the set point value (i.e, 500/3=167 lux), the fraction of light to be switched ON is equal to one. For illuminance values between onethird and twothirds of 500 (i.e., 167333 lux), the fr action of light to be switched ON is equal to twothirds. This means if the light level in the room was between 167 and 333 lux, twothirds of the lights were be switched ON. If the illuminance value in the room was between 333 and 500 lux, then onethirds of the lighting was switched ON. Finally, if the illuminance value in the room is more than 500 lux, all the lights were switched OFF. Table 3 1 describes the fractional distribution of electric lighting used, depending on the illuminance values in the ro om.

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33 Selecting Input and Weather Files As shown in Figure 36, the input file and weather files can be selected from the Single Simulation tab from the two pull down lists. The "Browse" option can be used to locate an input or weather file that was creat ed or downloaded from the website. Table 31. Example of a Stepped Lighting Control System with Three Steps Daylight illuminance (lux) Fraction of lights that are ON 0 167 1.0 167 233 2/3 233 500 1/3 500 0.0 Figure 36 The first drop down menu is used for selecting the input file (IDF editor file) created using IDF Editor.

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34 Figure 37 shows the dropdown menu to select the weather file for the city for which the simulation was run. The weather files can be downloaded from the website for the B uilding T echnologies P ro gram of the U.S Department of Energy: Energy Efficiency and Renewable Energy. The simulate option at the bottom of the interface is used to run the simulations. Figure 37 The second dropdown menu is used for selecting the weather file. Radiance The Radiance program was developed as a research tool for predicting the distribution of visible radiation in illuminated spaces. Radiance takes as input a threedimensional geometric model of the physical environment and produces a map of

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35 spectral radiance v alues in a color image. The technique of ray tracing follows light backwards from the image plane to the source(s). Because Radiance can produce realistic ima ges from a simple description, it has a wide range of applications in graphic arts, lighting desig n, C omputer A id ed Design (CAD), and architecture ( Larson and Shakespeare 1998) The simulation tool Radiance uses a light backwards ray tracing method with extensions to efficiently solve the rendering equation for specular, diffuse and directional diffuse reflection and transmission in any combination to any level in any environment, including complicated, curved geometries. The simulation blends deterministic and stochastic ray tracing techniques to achieve the best balance between speed and accuracy in its local and global illumination methods ( Larson 1991). The building blocks for any R adiance input file are the primitives All the materials that were used, a long with all the surfaces that describe d the objects in the scene, nee d to be described as prim itives. The scene description for R adiance is in threedimensional Cartesian (X, Y, Z rectilinear) coordinates which list s all the materials and surfaces used to create the scene. The scene description file is stored in American Standard Code for Information Interchange (ASCII) text. Modifiers are either the word "void, which indicates no modifier, or some previously defined primitive identifier. The type can be the material type (e.g. plastic, metal or glass) or surface type (e.g. sphere, polygon, cone, or cylinder), as well as one of a few other type categories (pattern, texture, or mixture), and the identifier is the nam e of the primitive being described.

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36 Figure 38 shows a fisheye image of the office room used in this case study. This image was generated by the software Radiance. This is a high resolution fisheye image of the office room used in the case study. This image was rendered in Radiance using the command Rpict (A ppendix B). The walls of the office room are gr a y, the floor was green, and the two furniture tables shown are made of wood. Figure 38 Fish eye view of a Radiance rendered image of the office room

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37 Figure 39 shows a closer view of the o ffice room. The V enetian blinds covering the window can be observed in the image. Figure 39 Close up view of the office room. Quality settings T o obtain meaningful quantitative results and maintain the photometric accuracy, the R adiance quality setting needs to be configured c orrectly The following three main

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38 ambient parameters were considered for this research to calculate the illuminance values at the four sensor points. 1) Ambient bounces ( ab) 2) Ambient value ( av) 3) Ambient divisions ( ad) Ambient bounces ( ab): This parameter sets the number of inter reflections between the surfaces that the program should calculate before reverting to the ambient value. For example, if the ambient bounce ( ab) was set to 4 then the sampling of the ray occurs on four different su rfaces after leaving the source. Thus as the ab value increases, the ambient bounce increases and the accuracy of the quantitative results increases. Concurrently, the time taken to run the simulations also increases. Due to the large quantity of the data desired a low value of ambient bounce ( ab) was assumed for this research. Ambient value ( av): The ambient value is the average radiance in all directions of the visible scene, and it is to be defined by the user as an RGB value. Setting an ambient value may be useful for visualization, where adding a constant radiance to a scene may save on computational effort by maybe achieving a similar appearance using less ambient bounces Where absolute quantitative accuracy is required, the ambient value should be set at zero, and all simulated lighting should be a result of the indirect calculation. Ambient Divisions ( ad) Monte Carlo sampling takes place in cosineweighted hemispher es at selected points within the model, with interpolation occurring between these points. The number of ambient divisions sets the number of samples sent out from each sample hemisphere. The error in the Monte Carlo calculation of i ndirect illuminance is inversely

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39 proportional to t he square root of this number. In other words, the higher the value for ad, more accurate the results are. The ad value of 4096 was used in this research. Conventionally ad value is ex pressed as a factor of 2 (2n). In conclu sion, an appropriate setting of the ambient parameters was one of the challenging aspects of using Radiance. The need for r ealistic and meaningful results necessitates higher settings of ambient parameters. Higher ambient parameters demand for high computer a processing capability, which means higher investments in computer infrastructure. It was important to maintain a balance between the research costs and time required for running the simulations. Time constraints as well as low computer processing capa bilities meant a compromise with the a mbient settings of the software ( Larson and Shakespeare 1998).

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40 CHAPTER 4 RESULTS AND ANALYSIS Introduction Simulations were performed by EnergyPlus (EP) and Radiance to assess the thermal and daylighting performance of the proposed split controlled blind system. The simulations were carried out for seven different static slat angles (0 15 30 45 60 75 and 90 ) in th e case of conventional blinds. In the case of split controlled blinds 29 set s of split angles were tested. The parameters average hourly lighting load (W), a verage hourly HVAC load (W) and average t otal e nergy consumption (W) were obtained from EnergyPlus for three days of the year : March 21, June 21, and December 21. A comparative study was conducted for the results obtained from EnergyPlus and Radiance to assess the performance of the two blind systems. Illuminance values were obtained at the four sensor points by from the Radiance simulations conducted for the entire year. The four sensor point s were placed one meter from each other with the first sensor point placed one meter from the window. EnergyPlus Results Analysis Energy consumption results were obtained from the software EnergyPlus Simulations were performed for the three days of the year, Marc h 21, June 21 and December 21 for each occupied hour from 8 a.m. to 5 p.m. The results obtained are tabulated in Table 41 The hourly lighting energy consumption, HVAC consumption, and as the total energy consumption w ere used as criteria for selection of the best blind angle for that hour (refer to choosing angle for best angle). Table 41 gives the energy consumption for lighting, HVAC, and the total electric demand for the three days March 21, June 21, and December 21 for each occupied

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41 hour from 8 a.m. to 5 p.m. The results are shown for the best possible angle for each hour. For example, if 30 is the best angle for conventional blinds at 9 a.m. on March 21 and 10 0 45 is the best angle for split blinds for the same hour and day, then the Lighting consumption, HVAC and Total electric demand for that hour for that day were chosen. Table 42 shows the comparative study of split controlled blinds and conventional blinds. The three columns give the difference in percentage for the lighting energy consumption, HVAC and total electric consumption between the split controlled blinds and conventional blinds. A positive difference would mean split controlled blinds work better than conventional blinds, and a negative difference would infer otherwis e. Figure s 4 1 through 43 show the percentage of energy savings as a result of using split controlled blinds. The x axis represents the office hours from 9 a.m. to 5 p.m. The y axis represents the percentage values. A positive difference indicates energy savings as a result of using split controlled blinds. A negative difference indicates energy savings as a result of using conventional blind system blind system. On March 21 ( Figure 4 1), 11 a.m. lighting energy consumption for the split controlled blinds angle was 50% more than for conventional blinds and the HVAC consumption was 2.83% more than that of conventional blinds. The total energy consumption for that hour was 14.81% more in the case of split controlled blinds as compared to conventional blinds At 1 p.m., the split controlled blind system was 50% more efficient in terms of lighting energy consumption and 1.44% more efficient in terms of HVAC consumption. Overall at 1 p.m., the split controlled blinds were 13.22% more efficient than conventional blinds. Considering the results for the entire day, a marginal

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42 difference of 1% occurred between the two systems with split controlled blinds being more efficient. For June 21 at noon ( Figure 4 2), the split controlled blinds had 33.33% lower electric lighting energy consumption and 20.81% lower HVAC consumption thus total energy consumption for split controlled blinds was 24.41% lower as compared to conventional blinds. On average for the entire day, split controlled blinds caused 3.10% less energy consumption, as compared to conventional blinds. For December 21 ( Figure 4 3) the average energy consumption in the case of the split controlled blind system was 1.85% less than the conventional blinds. Thus on average the split controlled blind system worked slightly better than the conventional blind system in terms of l ighting and HVAC electric energy consumption. Daylighting Analysis Evaluating the performance of blinds was one of the important tasks of this research. Dynamic daylighting performance parameter such as Useful Daylight Illuminance (UDI) was recommended by Mardaljevic et al. (2006). The UDI may be defined as the percentage of time per year when useful daylight ( i.e., between 100 lux and 2000 lux) is available to the occupant. If illumina nce value in the room is less than 100 lux, the room becomes too dark and when greater than 2000 lux, the excess daylight can cause glare and thus discomfort to the occupant ( Mardaljevic et al. 2006). Once the illuminance values on a work plane are obtained, the next challenge was to quantify these results for comparative study. The D aylight A utonomy (DA) is the percentage of time per year when the minimum required illuminance at the sensor point is met by daylight alone ( Mardaljevic et al. 2006).

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43 Ta ble 41. Energy consumption for lighting (W), HVAC (W) and Total Electric Demand (W) for March 21, June 21 and December 21 Conventional Blinds Split blinds 21-Mar Angle Hour Lighting Demand HVAC Demand Total Electric Demand Angle Hour Lighting Demand HVAC Demand Total Electric Demand 30 9 162 263 425 10-0-45 9 162 263 425 30 10 115 282 398 10-45-45 10 128 284 412 30 11 102 301 403 80-80-80 11 154 309 463 30 12 102 312 415 80-80-80 12 102 317 419 30 13 102 320 422 37-45-45 13 51 315 366 30 14 102 318 420 37-45-45 14 77 315 392 30 15 102 309 411 10-45-45 15 102 308 411 0 16 102 294 397 10-0-0 16 102 294 397 0 17 141 279 420 0-0-0 17 141 246 387 21-Jun Angle Hour Lighting Demand HVAC Demand Total Electric Demand Angle Hour Lighting Demand HVAC Demand Total Electric Demand 15 9 188 390 578 10-0-0 9 188 390 578 15 10 188 393 581 10-0-0 10 188 393 581 15 11 162 394 557 10-0-0 11 154 394 547 30 12 154 395 549 10-45-0 12 102 313 415 15 13 154 397 551 10-0-0 13 154 397 551 15 14 154 397 551 10-0-0 14 154 397 551 15 15 162 397 559 10-0-0 15 154 396 550 15 16 188 399 587 10-0-0 16 188 399 587 15 17 188 400 588 10-0-0 17 188 399 587 21-Dec Angle Hour Lighting Demand HVAC Demand Total Electric Demand Angle Hour Lighting Demand HVAC Demand Total Electric Demand 60 9 162 1455 1618 0-80-80 9 196 1452 1648 60 10 115 1342 1457 37-80-0 10 64 1361 1425 60 11 102 1261 1363 0-80-80 11 102 1244 1346 60 12 102 1212 1314 0-80-80 12 51 1209 1260 60 13 102 1186 1288 37-45-80 13 51 1228 1279 60 14 102 1194 1296 0-80-80 14 0 1195 1195 60 15 102 1230 1333 0-80-45 15 51 1232 1283 0 16 13 1286 1299 0-0-45 16 26 1286 1311 0 17 85 1352 1437 0-0-45 17 90 1353 1442

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44 Table 42. EnergyPlus Comparison Results Simulation day Hours Lighting energy consumption (Difference in percentage) HVAC electric demand (Difference in percentage) Total Electric Demand (Difference in percentage) 21-Mar 9 0.00 0.04 0.02 10 -11.11 -0.47 -3.56 11 -50.00 -2.83 -14.81 12 0.00 -1.38 -1.04 13 50.00 1.44 13.22 14 25.00 0.94 6.80 15 0.00 0.08 0.06 16 0.00 0.08 0.06 17 0.00 11.67 7.76 Average 1.54 1.06 0.95 21-Jun 9 0.00 0.05 0.03 10 0.00 0.02 0.02 11 5.26 0.20 1.68 12 33.33 20.81 24.31 13 0.00 0.00 0.00 14 0.00 0.02 0.02 15 5.26 0.22 1.68 16 0.00 0.10 0.07 17 0.00 0.11 0.07 Average 4.87 2.39 3.10 21-Dec 9 -21.05 0.27 -1.87 10 44.44 -1.39 2.24 11 0.00 1.34 1.24 12 50.00 0.28 4.16 13 50.00 -3.60 0.66 14 100.00 -0.12 7.79 15 50.00 -0.11 3.74 16 -100.00 0.01 -0.97 17 -5.00 -0.08 -0.37 Average 18.71 -0.38 1.85

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45 Figure 41 Energy consumption differences between split controlled blinds and conventional blinds for March 21st

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46 Figure 42 Energy consumption differences between split controlled blinds and conventional blinds for June 21st.

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47 Figure 43 Energy consumption differences between split controlled blinds and conventional blinds for December 21

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48 Mardaljevic et al. (2006) suggested that the useful daylight illuminance can be divided into two ranges, UDI between 100 lux and 500 lux can be considered as supplementary daylight, which meant, depending on user preference, electric lighting could be used to supplement daylight required in the room T he second range was named as autonomous UDI for illuminance values between 500 lux and 2000 lux. This range of daylighting would not require any artificial electric li ghting. Choosing Blind A ngle One of the objectives of choosing the blinds best possible slat angle was to make sure sufficient useful daylight was available at each sensor point. The angle of the blind s setting which gave maximum illuminance levels on all four sensor points was considered the best angle for the blinds setting. Thus the basic algorithm for selecting the best angle was as follows: Select all the angles at the first sensor point which have illuminance levels in the autonomous UDI range (5002000 lux). Then select the same angles for the fourth sensor point Choose the angle that gives maximum illuminance value for the fourth sensor point Then select the angle among the points chosen from the fi rst sensor point which has given the highest illuminance value for the fourth sensor point This way the blind angle for each hour was chosen. T hen the UDI was classified for each sensor point, depending on the value of illuminance at each sensor point F or example, if the first sensor point gave an illuminance value between 500 lux and 2000 lux; it was classified into the 500 lux to 2000 lux UDI range. Similarly, if a sensor point gave an illuminance value between 100 lux and 500 lux, then it was classifi ed as the UDI in the 100 lux to 500 lux range. For an illuminance value between 100 lux and 500

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49 lux, the value 100% was assigned to the corresponding UDI range, and a value of zero was assigned to the remaining UDI ranges. For each hour, the UDI was defined and the average for each day was calculated. Similarly, the average for each month was calculated and tabulated into the following results. Tables 43 through 47 show the UDI results for each month of the year, and are tabulated for each of the four se nsor points. The UDI is divided into four sections: UDI<100 lx, UDI 100500lx, UDI 5002000lx, and UDI> 2000 lx. The values in Tables 43 through 47 show the average UDI values for each month. Radiance R esult s A nalysis Figures 44 through 47 show UDI distribution for the four sensor points in the form of stacked column charts. The x axis represents the months and the y axis represents the percentages (i.e. values of UDI). Different UDI ranges are represented by different colors. At the first sensor point ( Figure 44), for the month of February, the UDI (5002000 lux) decreases to 92% and for the month of March it decreases to 84% in the case of split controlled blinds. In the case of conventional blinds the UDI remain s almost constant throughout the year except for the month of October when the UDI (5002000 lux) decreases to 93 % Thus in this case for the first sensor point, the conventional blinds have an upper edge over the blinds in terms of daylighting performanc e. At the second sensor point ( Figure 45), for the month of March, the UDI (5002000 lux) decreases to 77% and for the month of October, it decreases to 80% while maintaining a constant value between 90% to 96 % for the rest of the year in the case of spl it controlled blinds. In the case of conventional blinds the UDI remains almost

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50 Table 43. UDI values for Sensor Point 1 SPLIT-CONTROLLED BLINDS SENSOR POINT 1 UDI <100lx UDI =100lx-50 UDI =500-200 UDI >2000lx jan 0.00 0.32 99.68 0.00 feb 0.00 7.24 92.76 0.00 mar 0.00 15.31 84.69 0.00 apr 0.00 0.00 100.00 0.00 may 0.00 0.00 100.00 0.00 jun 0.00 0.00 100.00 0.00 jul 0.00 0.00 100.00 0.00 aug 0.00 0.00 100.00 0.00 sep 0.00 3.33 96.67 0.00 oct 0.00 9.35 90.65 0.00 nov 0.00 0.33 99.67 0.00 dec 0.00 0.32 99.68 0.00 Average 0.00 3.02 96.98 0.00 CONVENTIONAL BLINDS SENSOR POINT 1 UDI <100lx UDI =100lx-50 UDI =500-200 UDI >2000lx jan 0.00 0.00 98.39 1.61 feb 0.00 0.00 100.00 0.00 mar 0.00 0.00 100.00 0.00 apr 0.00 0.00 100.00 0.00 may 0.00 0.00 100.00 0.00 jun 0.00 0.00 100.00 0.00 jul 0.00 0.00 100.00 0.00 aug 0.00 0.00 100.00 0.00 sep 0.00 0.00 100.00 0.00 oct 0.00 6.77 93.23 0.00 nov 0.00 0.00 100.00 0.00 dec 0.00 0.00 98.39 1.61 Average 0.00 0.56 99.17 0.27

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51 Table 44. UDI values for Sensor Point 2 SPLIT-CONTROLLED BLINDS SENSOR POINT 2 UDI <100lx UDI =100lx-50 UDI =500-200 UDI >2000lx jan 0 3.55 96.45 0 feb 0 10.00 90.00 0 mar 0 22.81 77.19 0 apr 0 10.00 90.00 0 may 0 10.00 90.00 0 jun 0 10.00 90.00 0 jul 0 10.00 90.00 0 aug 0 10.00 90.00 0 sep 0 13.33 86.67 0 oct 0 19.35 80.65 0 nov 0 9.00 91.00 0 dec 0 6.45 93.55 0 Average 0.00 11.21 88.79 0.00 CONVENTIONAL BLINDS SENSOR POINT 2 UDI <100lx UDI =100lx-50 UDI =500-200 UDI >2000lx jan 0.00 15.16 84.84 0.00 feb 0.00 3.10 96.90 0.00 mar 0.00 8.13 91.88 0.00 apr 0.00 10.00 90.00 0.00 may 0.00 10.00 90.00 0.00 jun 0.00 10.00 90.00 0.00 jul 0.00 8.71 91.29 0.00 aug 0.00 10.00 90.00 0.00 sep 0.00 10.00 90.00 0.00 oct 0.00 10.00 90.00 0.00 nov 0.00 10.33 89.67 0.00 dec 0.00 23.23 76.77 0.00 Average 0.00 10.72 89.28 0.00

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52 Table 45. UDI values for Sensor Point 3 SPLIT-CONTROLLED BLINDS SENSOR POINT 3 UDI <100lx UDI =100lx-500 UDI =500-2000 UDI >2000lx jan 0.32 14.52 85.16 0.00 feb 7.24 17.24 75.52 0.00 mar 15.31 22.19 59.69 2.81 apr 0.00 30.00 70.00 0.00 may 0.00 48.06 51.94 0.00 jun 0.00 49.00 51.00 0.00 jul 0.00 40.00 60.00 0.00 aug 0.00 36.45 63.55 0.00 sep 3.33 29.00 64.67 3.00 oct 9.35 20.00 70.65 0.00 nov 0.33 14.33 85.33 0.00 dec 0.32 17.74 81.94 0.00 Average 3.02 28.21 68.29 0.48 CONVENTIONAL BLINDS SENSOR POINT 3 UDI <100lx UDI =100lx-500 UDI =500-2000 UDI >2000lx jan 0.00 50.00 50.00 0.00 feb 0.00 20.34 79.66 0.00 mar 0.00 25.63 74.38 0.00 apr 0.00 31.00 69.00 0.00 may 0.00 43.23 56.77 0.00 jun 0.00 40.33 59.67 0.00 jul 0.00 40.00 60.00 0.00 aug 0.00 38.06 61.94 0.00 sep 0.00 29.67 70.33 0.00 oct 0.00 20.00 80.00 0.00 nov 0.00 29.33 70.67 0.00 dec 0.00 71.94 28.06 0.00 Average 0.00 36.63 63.37 0.00

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53 Table 46. UDI values for Sensor Point 4 SPLIT-CONTROLLED BLINDS SENSOR POINT 4 UDI <100lx UDI =100lx-500 UDI =500-2000 UDI >2000lx jan 0.32 94.52 5.16 0.00 feb 7.24 92.76 0.00 0.00 mar 15.31 71.25 10.94 2.50 apr 0.00 77.67 22.33 0.00 may 0.00 86.13 13.87 0.00 jun 0.00 90.00 10.00 0.00 jul 0.00 87.42 12.58 0.00 aug 0.00 84.19 15.81 0.00 sep 3.33 79.00 14.67 3.00 oct 9.35 81.94 8.71 0.00 nov 0.33 97.67 2.00 0.00 dec 0.32 86.13 13.55 0.00 Average 3.02 85.72 10.80 0.46 CONVENTIONAL BLINDS SENSOR POINT 4 UDI <100lx UDI =100lx-500 UDI =500-2000 UDI >2000lx jan 0.32 74.52 25.16 0.00 feb 0.00 67.59 32.41 0.00 mar 0.00 63.75 36.25 0.00 apr 0.00 73.00 27.00 0.00 may 0.00 89.68 10.32 0.00 jun 0.00 90.00 10.00 0.00 jul 0.00 90.00 10.00 0.00 aug 0.00 82.26 17.74 0.00 sep 0.00 62.00 38.00 0.00 oct 0.32 70.65 29.03 0.00 nov 0.00 62.33 37.67 0.00 dec 0.00 94.84 5.16 0.00 Average 0.05 76.72 23.23 0.00

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54 constant throughout the year except for the month of December, where it falls to 76% Thus in this case for the second sensor point both blind systems perform equally well. For the third sensor point ( Figure 46 ), the UDI (500 2000 lux) for split controlled blinds for the month of January was 85%, as compared to 50% for conventional blinds. For the month of November, the UDI for split controlled blinds was 85%, as compared to 70% for conventional blinds. Similarl y for the month of December, the UDI for split controlled blinds was 81%, as compared to 28% in the case of conventional blind system. Overall, for 68% time of the year, useful daylight was available at the third sensor point in the case of split controlle d blinds, as compared to 63% of time for conventional blinds. Figure 47 shows, at the fourth sensor point, that split controlled blind have a lower annual average UDI (5002000 lux) with the value of 10%, as compared to 23% for conventional blinds. Suppl ementary UDI (100 500 lux) was 88% for split blinds, as compared to 76% for conventional blinds, which means that the need for supplementary electric lighting would be required at the fourth sensor point for most of the year. By analyzing the UDI for both split and conventional blind systems, it can be concluded that both blind systems seem to perform equally well It was proposed that split controlled blinds would perform better than conventional blinds due to the different functions that the split sec tions would tend to perform. It cannot be determined why the proposed hypothesis was not true. It can be concluded that for the given set of split angles discussed in this research, both split controlled blinds and conventional blinds would perform equally well. Simulations performed with more varied set of angles can

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55 Figure 44. UDI comparison for Sensor Point 1(SP1).

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56 Figure 45. UDI comparison for Sensor Point 2 (SP2).

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57 Figure 46. UDI comparison for Sensor Point 3 (SP3).

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58 Figure 47. UDI comparison for Sensor Point 4 (SP4)

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59 perhaps shed more light on the performance of these split controlled blinds, as compared to conventional blinds. Figures 48 and 49 show the frequency distribution in percentage of total illuminance values for the entire year at the first sensor point for split controlled blinds and conventional blinds. X axis represents the illuminance values in range intervals of 100 lux and the y axis represents percentages of total illuminance values. For histograms sho wn in Figures 48 and 49, the illuminance values at the first sensor point are plotted for the entire year. In the case of conventional blinds a wide distribution of values falls in the range between 500 lux and 2000 lux. The illuminance values obtained f or split controlled blinds vary in a range of 600 lux to 2000 lux with almost 70% values falling in the range between 1400 lux and 2000 lux, thus showing less variation in illuminance values for split controlled blinds. Figure 410 shows the illuminance v alues at the first sensor point on January 1st at 8 a.m. The x axis represents the 29 split blind angles simulated, and the y axis represents the illuminance values obtained at the first sensor point for each of the split blind angles. For split blind angl es 37 80 80 37 80 45 where the top blinds are tilted downwards towards the interior at 37 ,illuminance values of 574 lux and 858 lux were obtained at the first sensor point respectively. This demonstrates the ability of the top blinds to deflect daylight into the room, even when the middle and bottom blinds are almost closed. For the angles 0 80 0 0 80 45 0 80 80 illuminance values of 851 lux, 849 lux, and 599 lux were obtained at the first sensor point respectively.

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60 With the middle blinds almost closed at 80 there is still sufficient daylight inside the room, which demonstrated the ability of the split blinds to prevent glare causing direct sunlight entering into the room at the eye level and concurrently have good daylighting insi de the room. Figure 411 shows the average illuminance values at all four sensor points in the office room. The x axis represents the four sensor points, and y axis represents the illuminance values in lux. Average illuminance value for split controlled blinds at the first sensor point is 1542 lux as compared to 1251 lux for conventional blinds. Average illuminance value for spilt controlled blinds at the first sensor point is 19% higher than the average illuminance value for conventional blinds. Similarl y, at the second sensor point, the illuminance value for split controlled blinds is 924 lux, as compared to 857 lux, for conventional blinds. Considering the comfort zone between 500 lux and 2000 lux, higher illuminance values were obtained at all four sensor points as a result of using split controlled blinds. Figures 412 through 4 15 show yearly illuminance (lux) distribution at the four sensor points respectively. The x axis represents months and y axis represents the illuminance values in lux. Figures 4 12 and 4 13 show that, from May 1st to September 1st, split controlled blinds consistently gave higher illuminance values at the first sensor point as compared to illuminance values at the first sensor point for conventional blinds. Figures 414 and 41 5 show higher illuminance values for conventional blinds as compared to split controlled blinds at the third and the fourth sensor point between April and September.

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61 Figure 48. Frequency distribution of illuminance values from split controlled blinds for sensor point 1. Figure 49. Frequency distribution of illuminance values from conventional blinds for the first sensor point.

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62 Figure 410. Illuminance values for all the split blind angles, at the first sensor point for January 1 at 8 a.m. Figure 411. Average illuminance distribution at the four sensor points.

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63 Figure 412. Yearly illuminance values at the first sensor point. Figure 413. Yearly illuminance values at the second sensor poin t

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64 Figure 414. Yearly illuminance values at the first sensor point. Figure 415. Yearly illuminance values at the first sensor point.

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65 Automation Model One of the aims of this research was to formulate a possible algorithm for the working of the split controlled blind system in real tim e. Figure 416 shows a possible algorithm for real time control of an automated split controlled blind system. The automation model describes the functioning of each section of the split control blind system with response to variation in illuminance levels inside the office room, as well as temperature variation. In the first possible condition, if the illuminance level inside the room was greater than the maximum allowed illuminance of 2000 lux, the bottom blinds would be completely closed to prevent any excess heat coming into the building. Simultaneously, the middle next step is to perform the illuminance check. In the second condition, if the illuminance level was less than 2000 lux, then the next illuminance check would be performed. Here the illuminance must be checked if it lies in the range between 500 lux and 2000 lux. If yes, no change in the blinds angle is suggested. If no, proceed to the temperature check. In the temperature check, if the temperature value is between the comfort ranges defined by the user, the bottom blinds are opened to allow light to come into the buildi ng until the illuminance value reaches the desired level. If the temperature is above the maximum allowed, the bottom blinds are closed, the middle section is closed by an e

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66 Figure 416. Automation algorithm for split controlled blinds.

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67 CHAPTER 6 CONCLUSIONS AND RECO MMENDATIONS Conclusions T he main purpose of this research was to determine the ability of split controlled blinds to provide better daylighting as compared to the conventional Venetian blind system, and, as a result, to provide comfort to the occupants. Energy Plus results showed light ing energy savings for the three days: March 21, June 21 and December 21. For certain periods of the day, lighting energy savings up to 50% were achieved by application of split controlled blind system. This results from the ability of split controlled blinds to provide better daylighting than the conventional blinds for the three days simulated. Daylighting analysis showed that on average the split controlled blind system provided higher illuminance at the sensor points compared to conventional blind syst em. Comparison of the UDI values for the two blind systems shows no significant difference among for the values for the four sensor points. It was observed that the average of all the illuminance values for the entire year at the four sensor points for the split controlled blinds were higher than those for the conventional blind system. From March to September, consistently higher illuminance values were obtained at the first and second sensor point in the case of split controlled blind system. In the case of the split controlled blinds, with the middle section of the blinds closed, sufficient daylight was obtained inside the room as a result of the ability of top section of the blinds to deflect light into the room. Thus, split controlled blind system can provide sufficient level of daylight inside the room. For split controlled blind system, 70% of the illuminance values obtained for the entire year at the first sensor point were within the range of

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68 14002000 lux. Conventional blinds showed a wide distribut ion of illuminance values at the first sensor point with values ranging from 500 lux to 2000 lux. Because of the constraints faced with running R adiance simulations, compromises were made on the quality setting s that were chosen for running the simulatio n. Higher quality settings w ould provide more realistic results. For daylighting analysis involving complex fenestration systems, the ambient bounces ( ab) value to be chosen is on the order of 6 to 7. Running simulations with such high quality settings is not feasible with traditional personal computers Recommendations It was observed that t he proposed split controlled blind system can be used to provide energy savings to the building. A wide range of split angles can be tested, which can verify the validity of split control blinds. The best possible angles can then be chosen and an experimental setup can be tested to verif y the validit y of Radiance and EnergyPlus results. The results obtained from the simulation carried out for this research provide the benchmark for further research in this area, and they also provide an insight into the use of advanced daylighting and energy simulatio n tools such as Radiance and EnergyPlus This research can be extended by performing glare studies. Glare index values can be computed for the four sensor points using Radiance to analyze and assess the performance of split controlled blinds in terms of occupancy comfort. This field of research contributes to the field of sustainable construction and can provide a valuable source of information for further research in this field. With an increased emphasis on g reen buildings, the need for more efficient building systems with advanced fenestration systems becomes a necessity

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69 APPENDIX A ENERGYPLUS SPECIFICATIONS Table A 1 shows the area distribution for each of the four walls along with their cardinal directions The building envelope mainly comprises of concrete walls with insulation. Table A 1. Window Wall Ratio Total North (315 to 45 deg) East (45 to 135 deg) South (135 to 225 deg) West (225 to 315 deg) Gross Wall Area (m2) 52.80 11.15 15.25 11.15 15.25 Window Opening Area (m2) 3.34 0.00 0.00 3.34 0.00 Window Wall Ratio (%) 6.33 0.00 0.00 30.00 0.00 The rate of heat loss is indicated in terms of the U factor (U value) of a window assembly. The I nsulating value is indicated by the R value which is the inverse of the U value. The lower the U value, the greater a window's resistance to heat flow and the better its insulating value. Table A 2 gives the reflectance value, U factor, gross area, azimuth angle, and the tilt angle for each of the surfac e elements of the office room. Table A 2. The exterior of the room Construction Reflectan ce U Factor with Film (W/m2 K) U Factor no Film (W/m2 K) Gross Area (m2) Azimuth (deg) Tilt (deg) CardinalDi rection WALL1 COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.751 2.37 15.25 270.00 90.00 W WALL2 COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.751 2.37 11.15 0.00 90.00 N WALL3 COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.751 2.37 15.25 90.00 90.00 E

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70 WALL4 COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.751 2.37 11.15 180.00 90.00 S SLAB COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.505 2.37 18.30 0.00 180.0 0 ROOF COMPOSITE CONCRETE/FOA M/CONCRETE WITH STEEL CONNECTORS 0.30 1.640 2.37 18.30 180.00 0.00 Tab le A 3 shows the area, U factor and Visible transmittance value for the fenestration used in the EnergyPlus Model simulation model. Construction Area of Opening (m2) U Factor SHGC Visible Transmittance Shade Control Parent Surface WINDOW DBL LOE (E2=.1) CLR 6MM/13MM AIR 3.34 2.69 0.567 0.744 Yes WALL4 Table A 4 shows the various Air flow rates used for the HVAC system. Table A 4. ( PTAC) PACKAGED TERMINAL: AIRCONDITIONER maximum cooling air flow rate [m3/s] maximum heating air flow rate [m3/s] maximum air flow rate when compressor is off [m3/s] maximum outside air flow rate in cooling [m3/s] maximum outside air flow rate in heating [m3/s] maximum outside air flow rate when compressor is off [m3/s] RINKER BUILDING OFFICE PTAC 0.090848 0.090848 0.090848 0.000000 0.000000 0.000000 Tables A 5 and A 6 s how the Rated Air Volume Flow, R ated Total Cooling Capacity, Rated Sensible Heating Ratio (SHR) value for the cooling coil and Nominal Capacity of the heating coil.

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71 Table A 5. COIL: DX: COOLING BYPASS FACTOREMPIRICAL Rated Air Volume Flow Rate [m3/s] Rated Total Cooling Capacity (gro ss) [W] Rated SHR RINKER BUILDING OFFICE PTAC COOLING COIL 0.090848 1503.86 0.798655 Table A 6. COIL: ELECTRIC: HEATING Nominal Capacity of the Coil [W] RINKER BUILDING OFFICE PTAC HEATING COIL 1666.43 Table A 7 shows the Design load, Design air flow, Temperature at peak, D ate / T ime of the peak used for the cooling load. Table A 7. HVAC Sizing Summary Zone Cooling Design Load (W) Calculated Design Air Flow (m3/s) User Design Air Flow (m3/s) Design Day Name Date/Time Of Peak Temperature at Peak (C) Humidity Ratio at Peak (kgWater/kgAir) RINKER BUILDING OFFICE 1108.53 0.091 0.091 SIMULATION DAY 1 6/21 13:30:00 33.00 0.00001 Table A 8 show the Design load, Design Air Flow, Temperature at peak, Date /Time of the peak used for the heating load. Table A 8. Zone Heating Design Load (W) Calculated Design Air Flow (m3/s) User Design Air Flow (m3/s) Design Day Name Date/Time Of Peak Temperature at Peak (C) Humidity Ratio at Peak (kgWater/kgAir) RINKER BUILDING OFFICE 1666.43 0.046 0.046 SIMULATION DAY 3 12/21 07:30:00 0.33 0.00368

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72 APPENDIX B: RADIANCE GENERATORS Generators A generator is any program that produces a scene description as its output. They usually appear as commands in a scene description file. An example of a simple generator is genbox. Genbox takes the arguments of width, height and depth to produce a parallelepiped description. Genprism takes a list of 2dimensional coordinates and extrudes them along a vector to produce a 3dimensional prism. Genrev is a more sophisticated generator that produces an object of rotation from parametric functions for radius and axis position. Gensurf tessellates a surface defined by the parametric functions x(s, t), y(s, t), and z(s, t). Genworm links cylinders and spheres along a curve. Gensky pr oduces a sun and sky distribution corresponding to a given time and date. Xform is a program that transforms a scene description from one coordinate space to another. Xform does rotation, translation, scaling, and mirroring. ry can be easily produced in RA DIANCE, without the help of any CAD program. Rview is ray tracing program for viewing a scene interactively. When the user specifies a new perspective, rview quickly displays a rough image on the terminal, and then progressively increases the resolution as the user looks on. The user can select a particular section of the image to improve, or move to a different view and start over. This mode of interaction is useful for debugging scenes as well as determining the best view for a final image. Rpict produc es a high resolution picture of a scene from a particular perspective. This program features adaptive sampling, crash recovery and progress reporting, all of which are important for timeconsuming images.

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73 APPENDIX C RADIANCE SCRIPT Appendix C shows the script used to run hourly simulations for each occupied hour from 8 a.m. to 5 p.m. for the 365 days of the year. Illuminance values were obtained at the four sensor points placed at a distance of 1 meter from each other with the first sensor point placed at a distance of 1 meter from the window at a height of 0.76 m from the floor level. #!/bin/csh f set hr=$1 set day=$2 set mon=$3 #set filename="conv15" if ($mon < 10) then set monname="0""$mon" else set monname=$mon endif if ($#argv > 3) then set anglename=$4 else set anglename="" endif if ($#argv > 4) then set angle1=$5 else set angle1="" endif if ($#argv > 5) then set angle2=$6 else set angle2="" endif if ($#argv > 6) then set angle3=$7 else set angle3="" endif

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74 set month= "$anglename""_""$monname"" ""$day"" ""$hr" echo $month set coord=( a 30.00 o 82.35 m 84) rm $month.out echo "hr=$hr" set skypar=($mon $day $hr $coord +s) echo "$month/$day/$hr">>$month.out gensky $mon $day $hr a 29.69 o 82.65 m 84 +s>sky1.rad oconv sky1.rad outside.rad room.rad window2.rad conv 45.rad>hr.oct mkillum ab 1 ad 256 as 128 av 0 0 0 hr.oct "<" windowillum1.rad>illumination.rad oconv i hr.oct illumination.rad>hour.oct rtrace h I ab 1 ad 4096 as 128 av 0 0 0 hour.oct>$month.out echo ">>$month.out rm hr.oct rm hour.oct end @ day++ echo "day=$day" end

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75 APPENDIX D ENERGYPLUS INPUTS Appendix D shows all the necessary inputs for running simulations inon EnergyPlus. All the data is obtained from .audit file, obtained as an output of EnergyPlus simulations. All the parameters and their assigned values, necessary to run the simulations can be seen here. 14 !=========== ALL OBJECTS IN CLASS: BUILDING =========== 15 16 BUILDING, 17 rinker building, !Building Name 18 0, !North Axis {deg} 19 Urban, !Terrain 20 0.04, !Loads Convergence Tolerance Value 21 0.4, !Temperature Convergence Tolerance Value {deltaC} 22 FullInteriorAndExteriorWithReflections, !Solar Distribution 23 25; !Maximum Number of Warmup Days 24 25 26 !=========== ALL OBJECTS IN CLASS: TIMESTEP IN HOUR =========== 27 28 TIMESTEP IN HOUR, 29 4; !Time Step in Hour 30 31 32 !=========== ALL OBJECTS IN CLASS: INSIDE CONVECTION ALGORITHM =========== 33 34 INSIDE CONVECTION ALGORITHM, 35 Detailed; !Algorithm 36 37 38 !=========== ALL OBJECTS IN CLASS: OUTSIDE CONVECTION ALGORITHM =========== 39 40 OUTSIDE CONVECTION ALGORITHM, 41 Detailed; !Algorithm 42 43 44 !=========== ALL OBJECTS IN CLASS: SOLUTION ALGORITHM =========== 45 46 SOLUTION ALGORITHM, 47 CTF, !SolutionAlgo 48 200; !Max Surface Temperature Limit 49 50 51 !=========== ALL OBJECTS IN CLASS: DIAGNOSTICS =========== 52

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76 53 DIAGNOSTICS, 54 DisplayExtraWarnings; !key1 55 56 57 !=========== ALL OBJECTS IN CLASS: RUN CONTROL =========== 58 59 RUN CONTROL, 60 Yes, !Do the zone sizing calculation 61 No, !Do the system sizing calculation 62 No, !Do the plant sizing calculation 63 Yes, !Do the design day simulations 64 Yes; !Do the weather file simulation 65 66 67 !=========== ALL OBJECTS IN CLASS: LOCATION =========== 68 69 Location, 70 Gainesville, !LocationName 71 30, !Latitude {deg} 72 81.65, !Longitude {deg} 73 5, TimeZone {hr} 74 54; !Elevation {m} 75 76 77 !=========== ALL OBJECTS IN CLASS: WEATHER STATION =========== 78 79 WEATHER STATION, 80 10.0, !Wind Sensor Height Above Ground {m} 81 0.14, !Wind Speed Profile Exponent 82 270.0, !Wind Speed Profile Boundary Layer Thickness {m} 83 1.5; !Air Temperature Sensor Height Above Ground {m} 84 85 86 !=========== ALL OBJECTS IN CLASS: DESIGNDAY =========== 87 88 DesignDay, 89 Simulation day 1, !DesignDayName 90 33, !Maximum DryBulb Temperature {C} 91 !Daily Temperature Range {deltaC} 92 !Humidity Indicating Conditions at Max DryBulb 93 101217, !Barometric Pressure {Pa} 94 8.8, Wind Speed {m/s} 95 10, !Wind Direction {deg} 96 0.9, !Sky Clearness 97 !Rain Indicator 98 !Snow Indicator 99 21, !Day Of Month 100 6, !Month 101 Thursday, !Day Type 102 !Daylight Saving Time Indicator 103 ; Humidity Indicating Type 104 105 DesignDay, 106 Simulation day 2, !DesignDayName

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77 107 29, !Maximum DryBulb Temperature {C} 108 Daily Temperature Range {deltaC} 109 !Humidity Indicating Conditions at Max DryBulb 110 101217, !Barometric Pressure {Pa} 111 8.8, !Wind Speed {m/s} 112 10, !Wind Direction {deg} 113 0.9, !Sky Clearness 114 !Rain Indicator 115 !Snow Indicator 116 21, Day Of Month 117 3, !Month 118 Tuesday, !Day Type 119 !Daylight Saving Time Indicator 120 ; !Humidity Indicating Type 121 122 DesignDay, 123 Simulation day 3, !DesignDayName 124 .33, !Maximum DryBulb Temperature {C} 125 !Daily Temperature Range {deltaC} 126 Humidity Indicating Conditions at Max DryBulb 127 101217, !Barometric Pressure {Pa} 128 8.8, !Wind Speed {m/s} 129 10, !Wind Direction {deg} 130 0.9, !Sky Clearness 131 !Rain Indicator 132 !Snow Indicator 133 21, !Day Of Month 134 12, !Month 135 Friday, !Day Type 136 !Daylight Saving Time Indicator 137 ; !Humidity Indicating Type 138 139 140 !=========== ALL OBJECTS IN CLASS: MATERIAL:REGULAR =========== 141 142 MATERIAL:REGULAR, 143 Composite Concrete/Foam/Concrete With Steel Connectors #3, !Name 144 Rough, !Roughness 145 0.15240030480061, !Thickness {m} 146 1.078, !Conductivity {W/mK} 147 2185.44, !Density {kg/m3} 148 1173, !Specific Heat {J/kgK} 149 0.9, !Absorptance:Thermal 150 0.7, Absorptance:Solar 151 0.7; !Absorptance:Visible 152 153 MATERIAL:REGULAR, 154 Composite Insulated Concrete Form Wall With Steel Ties #2, !Name 155 Smooth, !Roughness 156 0.101600203200406, !Thickness {m} 157 0.499, !Conductivity {W/mK} 158 1552.01, !Density {kg/m3} 159 880, !Specific Heat {J/kgK}

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78 160 0.9, Absorptance:Thermal 161 0.7, !Absorptance:Solar 162 0.7; !Absorptance:Visible 163 164 MATERIAL:REGULAR, 165 Composite Concrete/Foam/Concrete With Steel Connectors #1, !Name 166 Rough, !Roughness 167 0.101600203200406, !Thickness {m} 168 1.329, !Conductivity {W/mK} 169 2213.25, !Density {kg/m3} 170 964, !Specific Heat {J/kgK} 171 0.9, !Absorptance:Thermal 172 0.7, !Absorptance:Solar 173 0.7; !Absorptance:Visible 174 175 176 !=========== ALL OBJECTS IN CLASS: MATERIAL:WINDOWGLASS =========== 177 178 MATERIAL:WINDOWGLASS, 179 LoE CLEAR 6MM Rev, !Name 180 SpectralAverage, !Optical Data Type 181 !Name of Window Glass Spectral Data Set 182 0.006, !Thickness {m} 183 0.600, !Solar Transmittance at Normal Incidence 184 0.220, !Solar Reflectance at Normal Incidence: Front Side 185 0.170, !Solar Reflectance at Normal Incidence: Back Side 186 0.840, !Visible Transmittance at Normal Incidence 187 0.078, !Visible Reflectance at Normal Incidence: Front Side 188 0.055, !Visible Reflectance at Normal Incidence: Back Side 189 0.0, !IR Transmittance at Normal Incidence 190 0.10, !IR Hemispherical Emissivity: Front Side 191 0.84, !IR Hemispherical Emissivity: Back Side 192 0.9; !Conductivity {W/mK} 193 194 MATERIAL:WINDOWGLASS, 195 CLEAR 6MM, !Name 196 SpectralAverage, Optical Data Type 197 !Name of Window Glass Spectral Data Set 198 0.006, !Thickness {m} 199 0.775, !Solar Transmittance at Normal Incidence 200 0.071, !Solar Reflectance at Normal Incidence: Front Side 201 0.071, !Solar Reflectance at Normal Incidence: Back Side

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79 202 0.881, !Visible Transmittance at Normal Incidence 203 0.080, !Visible Reflectance at Normal Incidence: Front Side 204 0.080, !Visible Reflectance at Normal Incidence: Back Side 205 0.0, !IR Transmittance at Normal Incidence 206 0.84, !IR Hemispherical Emissivity: Front Side 207 0.84, !IR Hemispherical Emissivity: Back Side 208 0.9; !Conductivity {W/mK} 209 210 211 !=========== ALL OBJECTS IN CLASS: MATERIAL:WINDOWGAS =========== 212 213 MATERIAL:WINDOWGAS, 214 AIR 13MM, !Name 215 Air, !Gas Type 216 0.0127; !Thickness {m} 217 218 219 !=========== ALL OBJECTS IN CLASS: MATERIAL:WINDOWBLIND =========== 220 221 MATERIAL:WINDOWBLIND, 222 BLIND WITH HIGH REFLECTIVITY SLATS 1, !Name 223 HORIZONTAL, !Slat orientation 224 0.025, Slat width {m} 225 0.01875, !Slat separation {m} 226 0.001, !Slat thickness {m} 227 90, !Slat angle {deg} 228 0.9, !Slat conductivity {W/mK} 229 0.0, !Slat beam solar transmittance 230 0.8, !Slat beam solar reflectance, front side 231 0.8, !Slat beam solar reflectance, back side 2 32 0.0, !Slat diffuse solar transmittance 233 0.8, !Slat diffuse solar reflectance, front side 234 0.8, !Slat diffuse solar reflectance, back side 235 0.0, Slat beam visible transmittance 236 0.8, !Slat beam visible reflectance, front side 237 0.8, !Slat beam visible reflectance, back side 238 0.0, !Slat diffuse visible transmittance 239 0.8, !Slat diffuse visible reflectance, front side 240 0.8, !Slat diffuse visible reflectance, back side 241 0.0, !Slat IR (thermal) hemispherical transmittance

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80 242 0.9, !Slat IR (thermal) hemispherical emissivity, front side 243 0.9, !Slat IR (thermal) hemispherical emissivity, back side 244 0.050, Blindtoglass distance {m} 245 0.5, !Blind top opening multiplier 246 0.5, !Blind bottom opening multiplier 247 0.5, !Blind leftside opening multiplier 248 0.5, !Blind rightside opening multiplier 249 !Minimum Slat Angle {deg} 250 ; !Maximum Slat Angle {deg} 251 252 MATERIAL:WINDOWBLIND, 253 BLIND WITH HIGH REFLECTIVITY SLATS 2, !Name 254 HORIZONTAL, !Slat orientation 255 0.025, !Slat width {m} 256 0.01875, !Slat separation {m} 257 0.001, !Slat thickness {m} 258 90, !Slat angle {deg} 259 0.9, !Slat conductivity {W/mK} 260 0.0, !Slat beam solar transmittance 261 0.8, !Slat beam solar reflectance, front side 262 0.8, !Slat beam solar reflectance, back side 263 0.0, !Slat diffuse solar transmittance 264 0.8, !Slat diffuse solar reflectance, front side 265 0.8, !Slat diffuse solar reflectance, back side 266 0.0, !Slat beam visible transmittance 267 0.8, !Slat beam visible reflectance, front side 268 0.8, !Slat beam visible reflectance, back side 269 0.0, !Slat diffuse visible transmittance 270 0.8, !Slat diffuse visible reflectance, front side 271 0.8, Slat diffuse visible reflectance, back side 272 0.0, !Slat IR (thermal) hemispherical transmittance 273 0.9, !Slat IR (thermal) hemispherical emissivity, front side 274 0.9, Slat IR (thermal) hemispherical emissivity, back side 275 0.050, !Blindtoglass distance {m} 276 0.5, !Blind top opening multiplier 277 0.5, !B lind bottom opening multiplier 278 0.5, !Blind leftside opening multiplier 279 0.5, !Blind rightside opening multiplier 280 !Minimum Slat Angle {deg} 281 ; !Maximum Slat Angle {deg} 282 283 MATERIAL:WINDOWBLIND, 284 BLIND WITH HIGH REFLECTIVITY SLATS 3, !Name 285 HORIZONTAL, !Slat orientation

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81 286 0.025, !Slat width {m} 287 0.01875, !Slat separation {m} 288 0.001, !Slat thickness {m} 289 90, !Slat angle {deg} 290 0.9, !Slat conductivity {W/mK} 291 0.0, !Slat beam solar transmittance 292 0.8, !Slat beam solar reflectance, front side 293 0.8, !Slat beam solar reflectance, back side 294 0.0, Slat diffuse solar transmittance 295 0.8, !Slat diffuse solar reflectance, front side 296 0.8, !Slat diffuse solar reflectance, back side 297 0.0, !Slat beam visible transmittance 298 0.8, !Slat beam visible reflectance, front side 299 0.8, !Slat beam visible reflectance, back side 300 0.0, !Slat diffuse visible transmittance 301 0.8, !Slat diffuse visible reflectance, front side 302 0.8, !Slat diffuse visible reflectance, back side 303 0.0, !Slat IR (thermal) hemispherical transmittance 304 0.9, !Slat IR (thermal) hemispherical emissivity, front side 305 0.9, !Slat IR (thermal) hemispherical emissivity, back side 306 0.050, Blindtoglass distance {m} 307 0.5, !Blind top opening multiplier 308 0.5, !Blind bottom opening multiplier 309 0.5, !Blind leftside opening multiplier 310 0.5, !Blind rightside opening multiplier 311 !Minimum Slat Angle {deg} 312 ; !Maximum Slat Angle {deg} 313 314 315 !=========== ALL OBJECTS IN CLASS: CONSTRUCTION =========== 316 317 CONSTRUCTION, 318 Composite Concrete/Foam/Concrete With Steel Connectors, !Name 319 Composite Concrete/Foam/Concrete With Steel Connectors #3, !Outside Layer 320 Composite Insulated Concrete Form Wall With Steel Ties #2, !Layer #2 321 Composite Concrete/Foam/Concrete With Steel Connectors #1; !Layer #3 322 323 CONSTRUCTION, 324 Dbl LoE (e2=.1) Clr 6mm/13mm Air 1, !Name 325 LoE CLEAR 6MM Rev, !Outside Layer 326 AIR 13MM, !Layer #2 327 CLEAR 6MM, !Layer #3 328 BLIND WITH HIGH REFLECTIVITY SLATS 1; !Layer #4

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82 329 330 CONSTRUCTION, 331 Dbl LoE (e2=.1) Clr 6mm/13mm Air 2, !Name 332 LoE CLEAR 6MM Rev, !Outside Layer 333 AIR 13MM, !Layer #2 334 CLEAR 6MM, !Layer #3 335 BLIND WITH HIGH REFLECTIVITY SLATS 2; !Layer #4 336 337 CONSTRUCTION, 338 Dbl LoE (e2=.1) Clr 6mm/13mm Air 3, !Name 339 LoE CLEAR 6MM Rev, !Outside Layer 340 AIR 13MM, !Layer #2 341 CLEAR 6MM, !Layer #3 342 BLIND WITH HIGH REFLECTIVITY SLATS 3; !Layer #4 343 344 CONSTRUCTION, 345 Dbl LoE (e2=.1) Clr 6mm/13mm Air !Name 346 LoE CLEAR 6MM Rev, !Outside Layer 347 AIR 13MM, !Layer #2 348 CLEAR 6MM; Layer #3 349 350 351 !=========== ALL OBJECTS IN CLASS: ZONE =========== 352 353 ZONE, 354 Rinker building office, !Zone Name 355 0, !Relative North (to building) {deg} 356 0.203200406400813, !X Origin {m} 357 0, !Y Origin {m} 358 0, !Z Origin {m} 359 1, !Type 360 1, !Multiplier 361 3.04785126485828, !Ceiling Height {m} 362 autocalculate, !Volume {m3} 363 Simple, !Zone Inside Convection Algorithm 364 Simple, !Zone Outside Convection Algorithm 365 Yes; !Part of Total Floor Area 366 367 368 !=========== ALL OBJECTS IN CLASS: SURFACEGEOMETRY =========== 369 370 SurfaceGeometry, 371 LowerLeftCorner, !SurfaceStartingPosition 372 ClockWise, !VertexEntry 373 world; !CoordinateSystem 374 375 597 598 !=========== ALL OBJECTS IN CLASS: WINDOWSHADINGCONTROL =========== 599 600 WindowShadingControl, 601 Normal 1, !User Supplied Shading Control Name 602 InteriorBlind, !Shading Type 603 Dbl LoE (e2=.1) Clr 6mm/13mm Air 1, !Name of construction with shading 604 AlwaysOn, !Shading Control Type

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83 605 !Schedule Name 606 !SetPoint {W/m2, W or deg C} 607 Yes, !Shading Control Is Scheduled 608 Yes, !Glare Control Is Active 609 BLIND WITH HIGH REFLECTIVITY SLATS 1, !Material Name of Shading Device 610 FixedSlatAngle, !Type of Slat Angle Control for Blinds 611 ; !Slat Angle Schedule Name 612 613 WindowShadingControl, 614 Normal 2, !User Supplied Shading Control Name 615 InteriorBlind, !Shading Type 616 Dbl LoE (e2=.1) Clr 6mm/13mm Air 2, !Name of construction with shading 6 17 AlwaysOn, !Shading Control Type 618 !Schedule Name 619 !SetPoint {W/m2, W or deg C} 620 Yes, !Shading Control Is Scheduled 621 Yes, !Glare Control Is Active 622 BLIND WITH HIGH REFLECTIVITY SLATS 2, !Material Name of Shading Device 623 FixedSlatAngle, !Type of Slat Angle Control for Blinds 624 ; Slat Angle Schedule Name 625 626 WindowShadingControl, 627 Normal 3, !User Supplied Shading Control Name 628 InteriorBlind, !Shading Type 629 Dbl LoE (e2=.1) Clr 6mm/13mm Air 3, !Name of construction with shading 630 AlwaysOn, !Shading Control Type 631 !Schedule Name 632 !SetPoint {W/m2, W or deg C} 633 Yes, !Shading Control Is Scheduled 634 Yes, !Glare Control Is Active 635 BLIND WITH HIGH REFLECTIVITY SLATS 3, !Material Name of Shading Device 636 FixedSlatAngle, !Type of Slat Angle Control for Blinds 637 ; !Slat Angle Schedule Name 638 639 640 !=========== ALL OBJECTS IN CLASS: SCHEDULETYPE =========== 641 642 ScheduleType, 643 Fraction, !ScheduleType Name 644 0.0 : 1.0, !range 645 CONTINUOUS; !Numeric Type 646 647 ScheduleType, 648 Any number; !ScheduleType Name 649 650 651 !=========== ALL OBJECTS IN CLASS: SCHEDULE:COMPACT =========== 652 653 SCHEDULE:COMPACT, 654 Office Lighting 2, !Name 655 Fraction, !ScheduleType 656 Through: 12/31, !Complex Field #1

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84 657 For: Weekdays SummerDesignDay, !C omplex Field #2 658 Until: 05:00, !Complex Field #3 659 0, !Complex Field #4 660 Until: 07:00, !Complex Field #5 661 0, !Complex Field #6 662 Until: 08:00, !Complex Field #7 663 0, !Complex Field #8 664 Until: 17:00, !Complex Field #9 665 1, !Complex Field #10 666 Until: 18:00, Complex Field #11 667 0, !Complex Field #12 668 Until: 20:00, !Complex Field #13 669 0, !Complex Field #14 670 Until: 22:00, !Complex Field #15 671 0, !Complex Field #16 672 Until: 23:00, !Complex Field #17 673 0, !Complex Field #18 674 Until: 24:00, !Complex Field #19 675 0, Complex Field #20 676 For: Saturday, !Complex Field #21 677 Until: 06:00, !Complex Field #22 678 0.05, !Complex Field #23 679 Until: 08:00, !C omplex Field #24 680 0.1, !Complex Field #25 681 Until: 12:00, !Complex Field #26 682 0.3, !Complex Field #27 683 Until: 17:00, !Complex Field #28 684 0.15, !Complex Field #29 685 Until: 24:00, !Complex Field #30 686 0.05, !Complex Field #31 687 For: WinterDesignDay, !Complex Field #32 688 Until: 24:00, Complex Field #33 689 0.0, !Complex Field #34 690 For: Sunday Holidays AllOtherDays, !Complex Field #35 691 Until: 24:00, !Complex Field #36 692 0.05; !Complex Field #37 693 694 SCHEDULE:COMPACT, 695 Office HVAC, !Name 696 on/off, !ScheduleType 697 Through: 12/31, !Complex Field #1 698 For: Weekdays SummerDesignDay, !Complex Field #2 699 Until: 06:00, !Complex Field #3 700 0.0, !Complex Field #4 701 Until: 22:00, !Complex Field #5 702 1.0, !Complex Field #6 703 Until: 24:00, !Complex Field #7 704 0.0, !Complex Field #8 705 For: Saturday WinterDesignDay, !Complex Field #9 706 Until: 06:00, !Complex Field #10 707 0.0, Complex Field #11 708 Until: 18:00, !Complex Field #12 709 1.0, !Complex Field #13 710 Until: 24:00, !Complex Field #14 711 0.0, !Complex Field #15 712 For: Sunday Holidays AllOtherDays, !Complex Field #16 713 Until: 24:00, !Complex Field #17

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85 714 0.0; !Complex Field #18 715 716 SCHEDULE:COMPACT, 717 ALWAYS 4, Name 718 Any number, !ScheduleType 719 Through: 12/31, !Complex Field #1 720 For: AllDays, !Complex Field #2 721 Until: 24:00, !Complex Field #3 722 4; Complex Field #4 723 724 SCHEDULE:COMPACT, 725 ALWAYS 20, !Name 726 Any number, !ScheduleType 727 Through: 12/31, !Complex Field #1 728 For: AllDays, Complex Field #2 729 Until: 24:00, !Complex Field #3 730 20; !Complex Field #4 731 732 SCHEDULE:COMPACT, 733 ALWAYS 24, !Name 734 Any number, ScheduleType 735 Through: 12/31, !Complex Field #1 736 For: AllDays, !Complex Field #2 737 Until: 24:00, !Complex Field #3 738 24; !Complex Field #4 739 740 SCHEDULE:COMPACT, 741 OCCUPY1, !Name 742 Fraction, !ScheduleType 743 Through: 12/31, !Complex Field #1 744 For: WeekDays SummerDesignDay CustomDay1 CustomDay2, !Complex Field #2 745 Until: 8:00, !Complex Field #3 746 0.0, !Complex Field #4 747 Until: 11:00, !Complex Field #5 748 1.00, !Complex Field #6 749 Until: 12:00, !Complex Field #7 750 0.80, !Complex Field #8 751 Until: 13:00, !Complex Field #9 752 1, !Complex Field #10 753 Until: 14:00, Complex Field #11 754 1, !Complex Field #12 755 Until: 18:00, !Complex Field #13 756 1.00, !Complex Field #14 757 Until: 19:00, !Complex Field #15 758 0.50, !Complex Field #16 759 Until: 21:00, !Complex Field #17 760 0.10, !Complex Field #18 761 Until: 24:00, !Complex Field #19 762 0.0, Complex Field #20 763 For: Weekends WinterDesignDay Holiday, !Complex Field #21 764 Until: 24:00, !Complex Field #22 765 0.0; !Complex Field #23 766 767 SCHEDULE:COMPACT, 768 ActSchd, !Name 769 Any number, !ScheduleType

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86 770 Through: 12/31, !Complex Field #1 771 For: AllDays, !Complex Field #2 772 Until: 24:00, Complex Field #3 773 50; !Complex Field #4 774 775 776 !=========== ALL OBJECTS IN CLASS: PEOPLE =========== 777 778 PEOPLE, 779 Occupancy, !Name 780 Rinker building office, !Zone Name 781 OCCUPY1, !Number of People SCHEDULE Name 782 people, !Number of People calculation method 783 2, !Number of People 784 !People per Zone Area {person/m2} 785 !Zone area per person {m2/person} 786 0.8, !Fraction Radiant 787 autocalculate, !user specified sensible fraction 788 ActSchd, !Activity level SCHEDULE Name 789 No, !Enable ASHRAE 55 comfort warnings 790 ZoneAveraged; !MRT Calculation Type 791 792 793 !=========== ALL OBJECTS IN CLASS: LIGHTS =========== 794 795 LIGHTS, 796 PERIMETER Lights 1, !Name 797 Rinker building office, !Zone Name 798 Office Lighting 2, !SCHEDULE Name 799 lighting level, Design Level calculation method 800 256, !Lighting Level {W} 801 20, !Watts per Zone Area {W/m2} 802 !Watts per Person {W/person} 803 0.2, Return Air Fraction 804 0.59, !Fraction Radiant 805 0.2, !Fraction Visible 806 1, !Fraction Replaceable 807 GeneralLights; !End Use Subcategory 808 809 810 !=========== ALL OBJECTS IN CLASS: DAYLIGHTING:DETAILED =========== 811 812 DAYLIGHTING:DETAILED, 813 Rinker building office, !Zone Name 814 2, !Total Daylighting Reference Points 815 1.524, !X coordinate of first reference point {m} 816 0.75, !Y coordinate of first reference point {m} 817 0.762, !Z coordinate of first reference point {m} 818 1.524, !X coordinate of second reference point {m} 819 3.5, !Y coordinate of second reference point {m}

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87 820 0.762, !Z coordinate of second reference point {m} 821 0.4, !Fraction of zone controlled by first reference point 822 0.6, !Fraction of zone controlled by second reference point 823 500, !Illuminance setpoint at first reference point {lux} 824 500, !Illuminance setpoint at second reference point {lux} 825 2, !Lighting control type 826 75, !Azimuth angle of view direction clockwise from zone yaxis (for glare calculation) {deg} 827 16, !Maximum allowable discomfort glare index 828 0.3, !Minimum input power fraction for continuous dimming control 829 0.3, Minimum light output fraction for continuous dimming control 830 3, !Number of steps (excluding off) for stepped control 831 1; !Probability lighting will be reset when needed in manual stepped control 832 833 834 !=========== ALL OBJECTS IN CLASS: NODE LIST =========== 835 836 NODE LIST, 837 zone node, !Node List Name 838 one, !Node_ID_1 839 two; !Node_ID_2 840 841 842 !=========== ALL OBJECTS IN CLASS: SET POINT MANAGER:SINGLE ZONE HEATING =========== 843 844 SET POINT MANAGER:SINGLE ZONE HEATING, 845 heat, Name 846 TEMP, !Control variable 847 99, !minimum supply air temperature {C} 848 99, !maximum supply air temperature {C} 849 Rinker building office, !zone name of the control zone 850 one, !node name of zone node 851 two, !node name of zone inlet node 852 zone node; !Name of the set point Node or Node List 853 854 855 !=========== ALL OBJECTS IN CLASS: SET POINT MANAGER:SINGLE ZONE COOLING =========== 856 857 SET POINT MANAGER:SINGLE ZONE COOLING, 858 cooling, !Name 859 TEMP, !Control variable 860 99, !minimum supply air temperature {C} 861 99, !maximum supply air temperature {C} 862 Rinker building office, !zone name of the control zone

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88 863 one, node name of zone node 864 two, !node name of zone inlet node 865 zone node; !Name of the set point Node or Node List 866 867 868 !=========== ALL OBJECTS IN CLASS: ZONE CONTROL:THERMOSTATIC =========== 869 870 ZONE CONTROL:THERMOSTATIC, 871 ZONE ONE Thermostat, !Thermostat Name 872 Rinker building office, !Zone Name 873 ALWAYS 4, !Control Type SCHEDULE Name 874 Dual Setpoint with Deadband, !Control Type #1 875 Office Thermostat Dual SP Control; !Control Type Name #1 876 877 878 !=========== ALL OBJECTS IN CLASS: DUAL SETPOINT WITH DEADBAND =========== 879 8 80 DUAL SETPOINT WITH DEADBAND, 881 Office Thermostat Dual SP Control, !Name 882 ALWAYS 20, !Heating Setpoint Temperature SCHEDULE Name 883 ALWAYS 24; !Cooling Setpoint Temperature SCHEDULE Name 884 885 886 !=========== ALL OBJECTS IN CLASS: COMPACT HVAC:THERMOSTAT =========== 887 888 COMPACT HVAC:THERMOSTAT, 889 Rinker thermostat, !Thermostat Name 890 ALWAYS 20, !Thermostat Heating Setpoint Schedule 891 24, !Thermostat Constant Heating Setpoint {C} 892 ALWAYS 24, !Thermostat Cooling Setpoint Schedule 893 20; !Thermostat Constant Cooling Setpoint {C} 894 895 896 !=========== ALL OBJECTS IN CLASS: COMPACT HVAC:ZONE:PTAC =========== 897 898 COMPACT HVAC:ZONE:PTAC, 899 Rinker building office, !Zone Name 900 Rinker thermostat, !Thermostat Name 901 autosize, !Zone Supply Air Cooling Flow Rate {m3/s} 902 autosize, !Zone Supply Air Heating Flow Rate {m3/s} 903 autosize, !Zone Supply Air NoL oad Flow Rate {m3/s} 904 !Zone Supply Air Sizing Factor 905 flow/zone, !Zone Outside Air Method

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89 906 0.00944, !Zone Outside Air Flow Rate per Person {m3/s} 907 !Zone Outside Air Flow per Zone Area {m3/sm2} 908 !Zone Outside Air Flow per Zone {m3/s} 909 !System Availability Schedule 910 Office HVAC, Supply Fan Operating Mode Schedule Name 911 Draw Through, !Supply Fan Placement 912 0.7, !Supply Fan Total Efficiency 913 75, !Supply Fan Delta Pressure {Pa} 914 0.9, !Supply Fan Motor Efficiency 915 Singlespeed DX, !Cooling Coil Type 916 !Cooling Coil Availability Schedule 917 autosize, !Cooling Coil Rated Capacity {W} 918 autosize, !Cooling Coil Rated SHR 919 3, !Cooling Coil Rated COP 920 Electric, !Heating Coil Type 921 !Heating Coil Availability Schedule 922 autosize, !Heating Coil Capacity {W} 923 0.8, !Gas Heating Coil Efficiency 924 ; !Gas Heating Coil Parasitic Electric Load {W} 925 926 927 !=========== ALL OBJECTS IN CLASS: REPORT VARIABLE =========== 928 929 Report Variable, 930 *, !Key_Value 931 Window Blind Slat Angle ,!V ariable_Name 932 hourly; !Reporting_Frequency 933 934 Report Variable, 935 *, !Key_Value 936 Lights Electric Power, !Variable_Name 937 hourly, !Reporting_Frequency 938 Office Lighting 2; !Schedule_Name 939 940 Report Variable, 941 *, !Key_Value 942 Daylight Illum at Ref Point 1, !Variable_Name 943 hourly; !Reporting_Frequency 944 945 Report Variable, 946 *, !Key_Value 947 Glare Index at Ref Point 1, !Variable_Name 948 hourly; !Reporting_Frequency 949 950 Report Variable, 951 *, !Key_Value 952 Total Electric Power Purchased, !Variable_Name 953 hourly; !Reporting_Frequency 954 955 Report Variable, 956 *, !Key_Value 957 Total Building Electric Demand, !Variable_Name

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90 958 hourly; !Reporting_Frequency 959 960 Report Variable, 961 *, !Key_Value 962 Total HVAC Electric Demand, !Variable_Name 963 hourly; !Reporting_Frequency 964 965 Report Variable, 966 *, !Key_Value 967 Total Electric Demand, !Variable_Name 968 hourly; !Reporting_Frequency 969 970 Report Variable, 971 *, !Key_Value 972 Daylight Illum at Ref Point 2, !Variable_Name 973 hourly; !Reporting_Frequency 974 975 Report Variable, 976 *, !Key_Value 977 Glare Index at Ref Point 2, !Variable_Name 978 hourly; !Reporting_Frequency 979 980 981 !=========== ALL OBJECTS IN CLASS: REPORT METER =========== 982 983 Report Meter, 984 PurchasedHeating:Facility, !Meter_Name 985 hourly; !Reporting_Frequency 986 987 Report Meter, 988 PurchasedCooling:Facility, !Meter_Name 989 hourly; Reporting_Frequency 990 991 992 !=========== ALL OBJECTS IN CLASS: REPORT CUMULATIVE METERFILEONLY =========== 993 994 Report Cumulative MeterFileOnly, 995 Electricity:Zone:RINKER BUILDING OFFICE, !Meter_Name 996 hourly; !Reporting_Frequency 997 998 Report Cumulative MeterFileOnly, 999 InteriorLights:Electricity, !Meter_Name 1000 hourly; !Reporting_Frequency 1001 1002 Report Cumulative MeterFileOnly, 1003 EnergyTransfer:Zone:RINKER BUILDING OFFICE, !Meter_Name 1004 hourly; !Reporting_Frequency 1005 1006 Report Cumulative MeterFileOnly, 1007 Heating:EnergyTransfer, !Meter_Name 1008 hourly; !Reporting_Frequency 1009 1010 Report Cumulative MeterFileOnly, 1011 Heating:EnergyTransfer:Zone:RINKER BUILDING OFFICE, !Meter_Name 1012 hourly; !Reporting_Frequency

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91 1013 1014 Report Cumulative MeterFileOnly, 1015 EnergyTransfer:Zone:RINKER BUILDING OFFICE, !Meter_Name 1016 hourly; !Reporting_Frequency 1017 1018 1019 !=========== ALL OBJECTS IN CLASS: REPORT =========== 1020 1021 Report, 1022 Variable Dictionary, !Type_of_Report 1023 DETAILS, !Name_of_Report 1024 Lines; !Specifications1_for_Report 1025 1026 Report, 1027 Construction; !Type_of_Report 1028 1029 Report, 1030 Surfaces, !Type_of_Report 1031 Details; !Name_of_Report

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92 LIST OF REFERENCES EnergyPlus Documentation (2008) Version 22. Galasiu A D., Atif M R., and Macdonald, R A. (2004). Impact of window blinds on daylight linked dimming and automatic on/off lighting controls. Solar Energy 76, 523544. Guillemin A., and Molteni S. (2002). An energy efficient controller for s hading devices self adapting to the user wishes. Building and environment 10911097. Kim S. Y. and Kim J. J (2007). The impact of daylight fluctuations on a daylight dimming control system in a small office. Energy and buildings 39, 935944. Koome y, J. G. Webber C. A., Celina S. Atkinson C. D., and Nicholls A (2001). Addressing energy related challenges for the US buildings sector: results from the clean energy futures study Energy Policy 29, 1209 1221. Larson C. (1991). Radiance User Manual Larson, G. W., and Shakespeare, R. (1998). Rendering with Radiance and Radiance Reference Material Morgan Kaufmann Publishers Lawrence, G. W. (1993). Radiance Tutorial. University of California. < http://radsite.lbl.gov/radiance/refer/tutorial. html > (15th November 1993) Lee E. S., and Selkowitz S. E (2006). The New York Times headquarters daylighting Mockup: Monitored performance of daylighting control system. Energy and Bui ldings 38 914 929. Lee, E. S. DiBartolomeo D. L. and Selkowitz S. E (1998) a Thermal and daylighting performance of an automated venetian blind and lighting system in a full scale private office. Energy and Buildings 29, 47 63 Lee, E. S., DiBartolomeo, D. L., Joseph, H. K ., Mehry, Y., and Selkowiz S. E (2006). Monitored Energy Performance of Electrochromic windows Controlled f or Daylight and Visual Comfort. P resented at the ASHRAE 2006 Summer Meeting, Quebec City, Canada, June 2428, 2006, and published in ASHRAE Transactions. Lee, E. S. DiBartolomeo D. L. Rubinstein, F. M. and Selkowitz S. E (2004). Low cost networking for dynamic window sy stems. Energy and Buildings 36, 503 513. Lee, E. S. DiBartolomeo D. L. Vine E. L. a nd Selkowitz S. E. (1998) b Integrated Performance of an Automated Venetian Blind/Electric Lighting System in a Full Scale Private Office. Proceedings of the Thermal Performance of the Exterior Envelopes of Buildings VII, 1998 in Clearwater Beach, FL.

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93 M ardaljevic., J. and Nabil, A. (2006)Useful daylight illuminances: A replacement for daylight factors Energy and Buildings 38 905 913. Reinhart C F (2004). Lightswitch 2002: A model for manual and automated control of electric lighting and blinds. Solar Energy 77, 1528. Selkowitz S. E., and Lee E. S. (2004) Integrating Automated Shading and Smart Glazings with Daylight Controls. International symposium on Daylight Buildings 1 9. Tzempeliko A Athienitis A K., and Karava, P. (2006). Simulation of faade and envelope design options for a new institutional building. Solar Energy 81 1088 1103. Tzempelikos, A., and Athienitis, A. K., ( 2007 ) The impact of shading design and control on building cooling & lighting demand. Solar Energy 81 (3), 369 382.

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94 BIOGRAPHICAL SKETCH Deepak V. Golasangimath was born in Bijapur, India. As a child, he excelled in mathematics and physics. This encouraged him to pursue an engineering career. Deepak earned his Master of Science (MS) degree in Building Construction from the M.E. Rinker, Sr. School of Building Construction at the University of Florida in Gainesville. While pursuing his masters degree in building construction, he worked as a research assistant with Dr. Svetlana Olbina. Prior to earning his M.S degree, he attended the National Institute of Technology in Warangal, India, to earn his Bachelor of Technology (B Tech) degree in Civil Engineering. Deepaks research interests are in the field of sustainable construction, with emphasis on building technologies. He is also interested in Building Information Management (BIM) and implementation of BIM in the building industry. After graduation in December 2009, Deepak plans to work for a construction management firm in the United States to pursue a successful career in the field of construction.