GUIDELINES FOR USING BUILDING INFORMATION MODELING (BIM) FOR ENVIRONMENTAL ANALYSIS OF BUILDINGS

Transcription

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

2 2012 Thomas J. Reeves 2

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

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

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS... 4 LIST OF TABLES... 8 LIST OF FIGURES ABSTRACT CHAPTER 1 INTRODUCTION Problem Statement Research Objectives Project Scope LITERATURE REVIEW Overview BEM Applications ASHRAE Standard Use of BEM in Conceptual Design Phase Use of BEM in Design Development Phase Use of BEM in Construction Documents Phase Use of BEM in Construction and Contracting Phase Use of BEM in Facilities Management Phase Integrating BEM with BIM BEM Capabilities Inputs Outputs Existing BEM Tools EnergyPlus equest Autodesk Ecotect Autodesk Green Building Studio Graphisoft EcoDesigner IES <Virtual Environment> (IES <VE>) Bentley Hevacomp Simulator Bentley Tas Simulator DesignBuilder Energy HEED Visual DOE Limitations of Building Energy Modeling

6 3 RESEARCH METHODOLOGY Initial Evaluation Case Study Re-evaluation of BEM Tools Used in the Case Study Developing Guidelines for BEM Selection and Application RESULTS Initial Evaluation User Friendliness Interoperability Available Inputs Available Outputs Cumulative Score Case Study Energy Usage Daylighting Performance Natural Ventilation Re-Evaluation of Building Energy Modeling Tools Used in the Case Study Guidelines for using Ecotect, Green Building Studio and IES<VE> Model Preparation in Revit Model Preparation in Building Energy Modeling Software Weather Data Acquisition Schedule Implementation Daylighting Analysis Natural Ventilation Analysis Results Analysis in the Building Energy Modeling Tools Guidelines for Using Building Energy Modeling Guidelines for Building Energy Modeling Application Guidelines for Building Energy Modeling Software Selection CONCLUSIONS AND RECOMMENDATIONS Conclusions Objective 1: Initial Evaluation Objective 2: Case Study Objective 3: Re-evaluation of BEM Tools Used in the Case Study Objective 4: Developing Guidelines for Using Building Energy Modeling Research Limitations Objective 1: Initial Evaluation Objective 2: Case Study Objective 3: Re-evaluation of the BEM Tools Used in Case Study Objective 4: Developing Guidelines for Using Building Energy Modeling Recommendations for Future Research

7 APPENDIX A INITIAL EVALUATION B CASE STUDY C GUIDELINES FOR USING BUILDING ENERGY MODELING REFERENCES BIOGRAPHICAL SKETCH

8 LIST OF TABLES Table page 3-1 Comparison of the buildings used in the case study Profiles of rooms compared for daylighting analysis Comparison of daylight factors for the selected rooms Natural Ventilation Simulation Results for three BEM tools. Potential energy savings from natural ventilation (kwh) Re-evaluation matrix with various weightings Re-evaluation of three BEM tools for interoperability Re-evaluation of three BEM tools for user friendliness Re-evaluation of three BEM tools for versatility Re-evaluation of three BEM tools for speed BEM tool use during conceptual design phase BEM tool use during design development phase BEM tool use during construction documents phase BEM tool use during construction and contracting phase BEM tool use during facilities management phase Recommended required inputs for BEM simulations in the different building lifecycle phases A-1 lnteroperability subcriteria checklist and raw scores A-2 User friendliness sub-criteria checklist and raw scores A-3 Available inputs subcriteria checklist and raw scores A-4 Available outputs checklist and raw scores A-5 Cumulative score with respective criteria scores B-1 Annual Energy Usage Rinker Hall (output of Green Building Studio simulation)

9 B-2 Annual Energy Usage Gerson Hall (output of Green Building Studio simulation) B-3 Natural Ventilation Gains Rinker Hall (output of Ecotect simulation) B-4 Natural Ventilation Gains Gerson Hall (output of Ecotect simulation) B-5 Natural Ventilation Potential Rinker Hall (output of Green Building Studio simulation) B-6 Natural Ventilation Potential Gerson Hall (Output of Green Building Studio simulation) C-1 Ecotect Guidelines and Recommendations Matrix C-2 Green Building Studio Guidelines and Recommendations Matrix C-3 IES<VE> Guidelines and Recommendations Matrix

10 LIST OF FIGURES Figure page 2-1 Information exchange in building design and delivery workflows. A) Traditional design/delivery. B) BIM-based collaboration. (Source: original) Example of BEM data flow (adapted from US General Services Administration 2009) GbXML files of buildings used in the case study exported from Revit Architecture. A) Rinker Hall. B) Gerson Hall Initial evaluation scoring system with criteria and subcriteria User Friendliness Interoperability Available Inputs Available Outputs Overall scores of the BEM tool initial evaluation The scores for available inputs and available outputs of the BEM tools Energy use intensity (EUI) comparison by building and by BEM tool. Dotted line denotes the CBECS national median EUI for educational building types (104 kbtu/sf) Energy use breakdown for two buildings used in case study using three BEM tools Diagram of building orientations relative to summertime prevailing winds Re-evaluation scoring system with criteria and subcriteria Re-evaluation un-weighted cumulative scores Location of weather data for three BEM tools in proximity to case study buildings Ecotect Schedule Editor Mean monthly average temperatures and corresponding comfort ranges. The shaded area refers to acceptable air-conditioned thermal comfort ranges, and the black lines refer to acceptable thermal range for natural ventilation. Dotted 10

11 lines denote the acceptable thermal comfort range for given mean monthly outdoor temperatures (ASHRAE 2004) IES<VE> schedule editor interface IES<VE> Modulating formula profile creation interface allows schedules to be derived from thermal parameters Green Building Studio run chart comparing buildings used in case study Workflow of energy modeling methodology employed in case study Guidelines for BEM software selection B-1 Rinker Hall energy use breakdown (output of Green Building Studio simulation) B-2 Rinker Hall annual fuel use breakdown (output of Green Building Studio simulation) B-3 Gerson Hall energy use breakdown (output of Green Building Studio simulation) B-4 Gerson Hall Energy Use Breakdown (output of Green Building Studio simulation)

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

13 CHAPTER 1 INTRODUCTION Building Information Modeling (BIM) efficiently integrates environmental analysis into the design and delivery of high-performance buildings. Building Energy Modeling (BEM), a subset of BIM, employs various simulation tools for analyzing the environmental performance of buildings. As the demand for high-performance buildings has increased, BEM has facilitated the delivery of buildings that meet expected performance requirements. The development of such tools has been integral to the process of integrated project delivery which tests and implements green building strategies from design to execution. By integrating BEM with the specialties of various other team members working around a centralized BIM model (e.g. structural, mechanical, architectural, planning), the process has the potential to become seamless. As sustainability becomes a standard practice in the building industry, the demand for high-performance buildings increases. Goals related to sustainability are being set ever higher, demanding greater levels of energy and resource efficiency (Bringezu, 2002). With the demand for high performance buildings and the resulting challenges posed to designers and builders, the integration of building performance analyses into the design and construction process becomes crucial. BIM in conjunction with BEM seeks to make this integration seamless throughout the design process (US General Services Administration 2005). BEM allows design professionals to predict how well a building will perform upon completion and provides greater insurance that designs will meet or exceed intended performance requirements (Krygiel & Nies 2008). By allowing design professionals to simulate building performance in a virtual environment, BEM tools provide feedback 13

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

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

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

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

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

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

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

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

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

23 orientation already established during the conceptual design phase, building energy modelers may begin to work directly off of more detailed BIM model design iterations (as opposed to re-creating the buildng geometry for every design iteration within the BEM platform). Building energy modelers can isolate a number of variables to evaluate and compare in more detailed analyses. Such variables may include glazing type (e.g. low-e, double glazed), visible transmittance of glass, glazing U-value, envelope constructions (with more detailed inputs for envelope layers and thermal properties), mechanical equipment, and building controls. For example, energy models can compare the daylighting benefits, energy savings, initial cost and lifecycle costs for two different models of windows based on manufacturer specifications. As decisions become finalized, BEM may also be used to estimate the actual energy performance of the building upon completion (US General Services Administration 2009) Use of BEM in Construction Documents Phase During the construction documents phase, designers use BEM to finalize estimations of building energy usage (US General Services Administration, 2009). These estimations may be used to demonstrate the design s code compliance and ability to save certain levels of energy in relation to a baseline model (as outlined by the methodology described by ASHRAE 90.1) in order to obtain sustainability assessment credits (e.g. LEED EA Credit 1) (US Green Building Council 2007). With BEM aiding in the selection of system manufacturers and suppliers, BEM material and building component databases are also helpful to develop schedules and performance requirements. 23

24 2.2.5 Use of BEM in Construction and Contracting Phase For contractors, BEM is especially useful for projects that must meet certain performance requirements. During the construction phase, BEM is used to assess the environmental impacts of change orders and to evaluate and compare the performances of different components when selecting manufacturers, subcontractors, and material suppliers (US General Services Administration 2009). For example, a performance requirement may demand that the project obtain LEED indoor environmental quality (IEQ) Credit 8.1. This credit is obtained if the project is able to provide adequate daylight to at least 75% of regularly occupied spaces. This may be obtained through the demonstration of computer simulation and a contractor may quickly test the models of various window manufacturers to estimate whether the system will meet IEQ 8.1 requirements. BEM is also useful for contractors in material documentation during the construction phase (Azhar et al. 2011). Material documentation is a necessity to obtain up to 12 LEED credit points related to reusable / recyclable material selection (Materials and Resources Credits), and non-toxic materials (Indoor Environmental Quality Credits). A recent case study by Azhar et al. (2011) demonstrated how BEM became useful by integrating into a Revit -based BIM workflow for the purposes of material documentation. The study exported the BIM model from Revit as a gbxml file and imported it into the BEM software IES<VE>. The report used the material takeoffs generated in Revit to provide outputs of reports comparing the model with the requirements for LEED credits. 24

25 2.2.6 Use of BEM in Facilities Management Phase The potential of BEM implementation into the facilities management and operation phases of the building lifecycle are still being explored. The General Services Administration (2009) describes one application of BEM in which the energy model is calibrated with metered data from actual building operation. System levels can then be adjusted in the virtual environment to identify errors in system operation and methods to optimize system performance. A similar approach may be taken to retrofit analysis in which a benchmark model is calibrated to simulate existing energy consumption, while iterative energy models are tested to identify measures that can improve energy efficiency. The integration of BEM into a real-time data feed is the next step for sustainability in facilities management. This has been demonstrated in an experiment by Clarke et al. (2002) in which the BEM simulations provided real time adjustments based on a live stream of measured data from the actual building. In this way, building systems may continuously be optimized based on how the building is being used over time. Platt et al. (2010) also demonstrated facilities managers can proactively optimize building energy consumption with the aid of energy modeling. Like the Clarke et al. s study, Platt et al. used a real-time data feed from measured data from actual building operation. Based on these inputs, an energy model was developed and calibrated with actual building performance. The energy model integrated a genetic algorithm to optimize system levels and reduce energy consumption Integrating BEM with BIM One of the major benefits of using BIM as opposed to traditional design methodologies, is that BIM allows for a team of experts from various fields to collaborate 25

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

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

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

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

30 Occupancy Schedule: The occupancy schedule is derived from the expected number of people inhabiting a building or room, and occupants presence throughout the day, week, and year. Different thermal zones may be assigned individual occupancy schedules. These values are typically input into the BEM tool as percentages of the maximum occupancy load per zone. While this type of schedule is fixed, the development of dynamic schedules has aided the integration of BEM tools into real-time analysis for facilities management (Kwow & Lee 2009). Operational Schedule: Operational schedules input the times at which building systems are being used and to what capacity (typically by percentage). Operational schedules may be assigned to such building systems as HVAC, lighting, and equipment (IES<VE> 2011). HVAC data: HVAC data includes the type of HVAC system intended to be used in the building, its efficiency, design fan flows, heating capacities, cooling capacities, and exhaust. This may also include estimated peak and off-peak times (Clark 2001). Required Indoor Temperature: Required indoor temperature is the temperature range to be maintained throughout the year, and is also referred to as thermal comfort range. This may be expressed as heating and cooling set points, and further described by a throttling range (the temperature threshold at which the HVAC is triggered on to maintain the intended temperature). ASHRAE 90.1 sets standard thermal ranges that must be maintained for occupant thermal comfort. Based on this input, some BEM tools will provide outputs on how many hours throughout the year the building and HVAC system is not able to meet the thermal comfort range. These are known as unmet hours. 30

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

32 Thermal Analysis: Thermal analysis is derived from simulations of heat transfer processes (i.e. convection, conduction, radiation) through the building and the building envelope. Thermal analysis includes temperature profiles and comfort studies of thermal zones (US Department of Energy 2011). Heating / Cooling Load Calculations: Heating and cooling load calculations are the amount of heat or heat removal over a given time to keep a building at a certain temperature. ASHRAE and the Chartered Institution of Building Services Engineers (CIBSE) calculation methods are the prominent models for heating and cooling loads. Typical units are in mbtu and kwh (Clark 2001). Airflow: Ventilation simulation may come in the form of natural, HVAC, and/or mixed-mode. These simulations use computational fluid dynamics (CFD) to assess the airflow in and around buildings and room objects. The common units for airflow simulations are miles per hour (mph) for natural ventilation, and cubic feet per minute (cfm) for HVAC simulations (Hensen 2003). Natural Ventilation: These simulations may be used to assess passive thermal gains from natural ventilation, or to estimate thermal losses due to infiltration (e.g. opening of doors). This may be assessed as a percentage of heating/cooling hours lost or gained due to natural ventilation, or as a factor of the amount of energy lost or gained. Some BEM tools allow users to implement operable window schedules that may be devised to simulate the optimized use of natural ventilation. Computational fluid dynamic (CFD) simulations may also be performed in some BEM tools. This is particularly important to estimate average airflow rates through spaces. CFD analysis is useful in microclimate analysis, in which isolated thermal zones may be assessed and designed 32

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

34 can rely on natural daylighting to light the spaces (Reynolds & Stein 2000). Daylight Autonomy calculation is preferred because it is less susceptible to inconsistencies in modeling methodology by taking data from various times of day throughout the year. Lighting Design: Simulates the energy efficiency and quality of electrical lighting in a building. This type of analysis may also estimate the annual energy consumption for lighting as it relates to a corresponding lighting or occupancy schedule. Typical outputs may be in units of kilowatt hours (kwh) (US Department of Energy 2011). Lifecycle Cost: Lifecycle analysis measures building cost, and a range of lifecycle costs such as capital, electricity and fuel costs, annual maintenance, repair costs, and may sometimes take inflation into account (Younker 2003). 2.4 Existing BEM Tools There are several building energy modeling tools available supporting a wide range of learning curves and capabilities. A survey conducted by Attia et al. (2009) found the top 10 BEM tools by use in the United States. These programs were EnergyPlus, EnergyPlus SketchUp Plugin, equest, Autodesk Ecotect, Autodesk Green Building Studio, IES<VE>, Visual DOE4.0, Design Builder, Energy10, and HEED. Of the 10 programs listed, the survey found Ecotect and equest to be the most widely used. The following sections outline these 10 programs along with three other major BEM tools: Bentley Hevacomp Simulator, Bentley Tas Simulator, and Graphisoft EcoDesigner EnergyPlus EnergyPlus is a module-based program that specializes in energy analysis and thermal load calculation. While a number of graphical interfaces are available to be used in conjunction with EnergyPlus, as a standalone program its inputs and outputs are 34

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

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

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

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

39 Disadvantages of Green Building Studio are : Unable to customize outputs Relies on third party software to model building geometry Graphisoft EcoDesigner EcoDesigner allows for immediate feedback pertaining to environmental performance during early design stages. It is a tool that is integrated into the Graphisoft ArchiCAD BIM platform. As such it allows energy analysis to be performed very quickly while designing in ArchiCAD. In addition to building geometry in ArchiCAD, EcoDesigner provides inputs for HVAC, location, and thermal properties of building envelope elements (Thoo, 2008). Features / Capabilities of EcoDesigner are: Strusoft Corecalculation engine (ASHRAE 90.1 compliant) ArchicCAD plugin Calculates energy consumption, carbon footprint, monthly energy breakdown Advantages of EcoDesigner are: Interoperability as a plugin to ArchiCAD User-friendly interface Quick calculation types Disadvantages of EcoDesigner are: Provides minimal opportunity for customization Relies on default values for many calculations Limited options for inputs and outputs IES <Virtual Environment> (IES <VE>) IES <VE> is a comprehensive BEM software that uses a set of modules to perform various calculations and simulations. These modules are all linked together by a common user interface and a single integrated data model. Modules included in the IES 39

40 <VE> package include ModelIT for building geometry creation, ApacheCalc for load analysis, ApacheSim for thermal analysis, MacroFlo for natural ventilation analysis, ApacheHVAC (HVAC simulation using components), SunCast for shading visualization, MicroFlo for three-dimensional computational fluid dynamic calculations, FlucsPro/Radiance for daylighting analysis, DEFT for model optimization, LifeCycle for life-cycle cost and energy analysis, and Simulex for building egress simulations (Crawley et al. 2008). Features / Capabilities of IES<VE> are: Outputs include energy usage, carbon emissions, thermal analysis, ventilation and airflow, solar analysis, daylighting, lifecycle analysis Building geometry editing and modeling Analysis grid gbxml model error check Advantages of IES<VE> are: Comprehensive building performance tool User-friendly interface Includes direct plugin to Revit to improve interoperability Disadvantages of IES<VE> are: Analysis results are saved in different folders gbxml model error check is required (Azhar, 2009) Bentley Hevacomp Simulator Hevacomp Simulator uses EnergyPlus as its simulation engine. As such it creates a connection between BIM platforms like Bentley and Revit and uses those as a graphical interface for EnergyPlus analyses. Hevacomp also provides compliance 40

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

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

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

44 HEED HEED is a free, user-friendly, single zone energy simulation program developed by UCLA. Its interface is largely wizard based, and 3d modeler is exceptionally easy to use. Relying only on floor area, number of stories, location, and building type as inputs, HEED generates two design iterations with one being 30% more energy efficient than the other. HEED can manage up to 9 iterations for 25 projects. Features / Capabilities of HEED are: Passive design inputs thermal mass, night ventilation, high-performance glazing Simulates energy usage, CO2 emissions, lifecycle cost Advantages of HEED are: User-friendly Provides detailed inputs Automatically generates design alternatives Disadvantages of HEED are: Limited to four thermal zones Limited HVAC options Weather data is limited to California Visual DOE 4.0 Visual DOE4.0 is a windows interface for the DOE2.1 building energy calculation engine. Users create the building geometry in Visual DOE by importing a DXF file of the floor plan from a CAD software and filling in the spaces using blocks in the model editor. Users can specify bulidng envelope constructions and HVAC system types from the libraries. Visual DOE also features a design alternative generator that can provide up to 99 different design iterations for building envelope and HVAC. Features / Capabilities of Visual DOE 4.0 are: Thermal and energy analysis 44

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

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

47 CHAPTER 3 RESEARCH METHODOLOGY The research methodology was broken down into three parts based on the three objectives. Section 3.1 describes the research methodology to conduct a cross evaluation of 12 major BEM tools. Section 3.2 provides the methodology of the case study; and section 3.3 describes the methodology for the re-evaluation and development of guidelines for using BEM for the environmental analysis of high performance buildings. 3.1 Initial Evaluation Twelve major BEM tools were selected for the initial evaluation. These programs were Graphisoft EcoDesigner, Bentley Tas Simulator V8i, Bentley Hevacomp Simulator V8i, Autodesk Ecotect, Autodesk Green Building Studio, IES <VE>, DesignBuilder, Visual DOE 4.0, Energy10, EnergyPlus, E-Quest and HEED. These BEM tools were selected based on a survey study by Attia et al. (2009) to identify the most widely used energy simulation software in the United States. The research included Bentley Tas Simulator V8i and Bentley Hevacomp Simulator V8i in addition to the top 10 BEM tools identified in the Attia et al. survey, as these are prevalent BEM tools used in the United Kingdom. The initial evaluation assesses these programs using four main criteria: interoperability, user-friendliness, available inputs, and available outputs. Within each criterion were a number of sub-criteria that were used as a checklist for each criterion. The sub-criteria were identified based on the literature review. Previous studies that compared the capabilities of existing BEM tools were synthesized into the sub-criteria checklists. These studies included Crawley et al. (2008), Attia et al. (2010) Azhar et al. (2009) and Azhar et al. (2011), as well as general 47

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

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

50 Table 3-1. Comparison of the buildings used in the case study Building Rinker Hall Gerson Hall Characteristics Date of completion Location Gainesville, FL Gainesville, FL Area of conditioned space 42,719 38,632 (sq. ft.) HVAC system Variable Air Volume with Energy Recovery Ventilation Building envelope construction (from exterior to interior) ¾ metal panel, 5.5 R20 cellulose insulation, 2 rigid insulation, ½ gypsum board Variable Air Volume with Terminal Reheat 4 brick veneer, 2 air gap / damproofing, 12 CMU, 5/8 GWB on 1-1/2 studs with rigid insulation Exterior wall U- Value Glazing type Low-E, double-glazed, Low-E, double-glazed insulated Glazing U-Value Window to Wall Area Ratio Albedo (Roof Reflectance) To compare the daylighting performance, four rooms from each building were selected (Table 3-2). The study compared similar rooms (similarities based on orientation, room area, and room function) between the two buildings for each of the three programs using daylight factor as the common unit. Ideally the study would have compared daylighting based on daylight autonomy, but could not due to limitations of the software. Because the two building have different orientations (the long axis of Rinker Hall is oriented east to west while the long axis of Gerson Hall is oriented north to south), the similarities between glazing orientations for room comparisons were limited. For instance, the rooms selected for comparison for the office room type and graduate studio room type had inconsistent glazing orientations because no such rooms exist in the two buildings that have the same room function and glazing orientation. The rooms selected were the closest fits of the rooms available for analysis. 50

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

52 First, the scoring system was used to select the best program based on an even weight applied to each of the four criteria. A matrix was then developed applying different weights to criteria based on order of importance for the potential user. In this way, a potential BEM user could use the matrix by first identifying the order of importance of the four criteria, and then be directed to the BEM tool most suitable to their preference. 3.4 Developing Guidelines for BEM Selection and Application Guidelines were organized by assessing the applicability of BEM to various building lifecycle phases. These building lifecycle phases were conceptual design, design development, construction documents, construction and contracting, and facilities management. Guidelines were developed based on the use of each BEM program during the case study. During the case study, the energy modeling methodology for each BEM tool was investigated. The steps in the modeling process under investigation were the following: model preparation in BIM (Revit Architecture) model preparation in BEM weather data acquisition schedule implementation energy analysis daylighting analysis natural ventilation analysis results analysis A log was maintained for each step documenting complications, advantages, disadvantages, observations and the locations of help files / user manuals / tutorial sources that were used for guidance during the energy modeling process. The spreadsheets for each BEM tool are available in Appendix C. 52

53 CHAPTER 4 RESULTS The results related to the four major objectives of the research are presented in the following sections. The initial evaluation identified IES<VE>, Ecotect, and Green Building Studio to be the most appropriate BEM tools out of the 12 evaluated. These three BEM tools were used in the case study to compare the energy usage, daylighting performance, and natural ventilation potential of Rinker Hall (a LEED gold- certified building), and Gerson Hall (a non-leed certified building). Overall, the results showed that Rinker Hall performed better than Gerson Hall in regard to energy usage and daylighting performance (for the rooms selected), while Gerson Hall performed better than Rinker Hall in natural ventilation potential. 4.1 Initial Evaluation The initial evaluation used the scoring system illustrated in Figure 4-1. The comprehensive score for each BEM tool was calculated as the sum of the individual criterion scores. The four criteria were interoperability, user friendliness, available inputs and available outputs. Each criterion was scored as the fraction of subcriteria supported by each BEM tool over the total number of subcriteria evaluated for each criterion. The criterion interoperability had five total subcriteria in the checklist. Thus the criterion score for interoperability was calculated as x/5, where x denotes number of subcriteria supported by the BEM tool. User friendliness had eight subcriteria and was calculated as x/8. Available inputs had 25 subcriteria and was calculated as x/25, and available outputs had 20 subcriteria and was calculated as x/20. 53

54 Figure 4-1. Initial evaluation scoring system with criteria and subcriteria Of the 12 BEM tools investigated in the initial evaluation, Ecotect, Green Building Studio, and IES<VE> scored the highest and were selected for use in the case study. The following sections illustrate the breakdown of the initial evaluation based on the four criteria (user friendliness, interoperability, required inputs, and versatility). Each section shows the breakdown of subcriteria that went into each BEM tool s score User Friendliness The results from the analysis showed that the most user friendly software of the 12 BEM tools evaluated, were Energy10, Green Building Studio, and HEED received the highest scores for user friendliness (Figure 4-2). Each of these BEM tools provided for six out of the eight sub-criteria included in the User Friendliness checklist. Ecotect, DesignBuilder, Visual DOE4.0 and IES<VE> each provided for five out of the eight sub-criteria. EQuest, EcoDesigner, Tas, Hevacomp, and EnergyPlus scored the lowest out of the 12 BEM tools providing for four out of the eight subcriteria in the User Friendliness checklist. 54

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

56 Studio is an Autodesk product, users also benefit from an extensive set of tutorials and user-forums for user-friendliness. EQuest, EcoDesigner, and EnergyPlus received low scores in this criterion (4 out of 7). These BEM tools had did not have simple user interfaces, had limited potential for customization, and did not provide feedback related to more environmentally friendly design alternatives. The Bentley BEM tools (Tas and Hevacomp Simulator ) also received low scores as these programs are tailored to complex yet specialized analyses, and are intended for use by qualified architects and engineers Interoperability IES <VE> scored highest (five out of five possible points) for interoperability. IES <VE> has capability of sharing information with each of the software / file types evaluated. These included interoperability with gbxml file types and Google SketchUp. EcoDesigner, Tas, Green Building Studio, and Hevacomp provided interoperability with all but SketchUp and scored four out of five. DesignBuilder and Visual DOE 4.0 allow DXF import to aid in the creation of building geometry, but 3D models must be developed in each program s in-house model builder. HEED and Energy10 demonstrated the lowest degree of interoperability with none of the programs or file types being supported by import or export capability. For these two programs, building geometry must be created within the BEM tool. The results for interoperability evaluation are illustrated in Figure

57 Figure 4-3. Interoperability Available Inputs IES<VE> had the highest score (20 out of 25) based on the available inputs evaluation, followed by Ecotect (19 out of 25) and equest (18 out of 25). HEED (score six out of 25), Energy10 (eight out of 25), Tas and Hevacomp (both scored nine out of 25) had the lowest scores regarding available inputs. The top three BEM tools in available inputs (IES<VE>, Ecotect, and equest ) may be considered the more versatile software. Users can input values for a wider range of variables into the model. While certain inputs were relatively constant for most of the software (building geometry, location, material properties), the inputs that set IES<VE>, Ecotect, and equest apart were more detail oriented. IES<VE> for example provides inputs for MEP models with HVAC component sizing, and plant data. IES<VE> and Ecotect both have lighting system inputs that provide users with the ability to design and simulate the effectiveness of electrical lighting. IES<VE>, Ecotect, and equest all have inputs for required internal temperature, type of energy used, occupancy and 57

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

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

60 Results of the overall scores are illustrated in Figure 4-6. IES<VE> received the highest score in the evaluation (3.38 out of 4). Ecotect received the second highest score (3.14 / 4), and Green Building Studio received the third highest (3.06 / 4). These three BEM tools were selected for use in the case study. Figure 4-6. Overall scores of the BEM tool initial evaluation Figure 4-7 depicts the overall versatility of the BEM tools in terms of available inputs and available outputs. BEM tools that had high scores in available inputs ( ) and available outputs (10-20) fell in quadrant B. BEM tools with higher scores in available outputs (10 20) and lower scores in available inputs (0 12.5) fell in quadrant A; tools with lower scores in available outputs (0 10) and higher scores in available inputs ( ) fell in quadrant C; and tools that had low scores in both available inputs (0 12.5) and available outputs (0 10) fell in quadrant D. BEM tools that were in quadrant A (higher scores in available outputs and lower scores in available inputs) included Energy10, Tas, Hevacomp, and Visual DOE4.0. BEM tools that were in quadrant B (higher scores for both available inputs and available outputs) were Green Building Studio, equest, Ecotect, and 60

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

62 rooms. Simulation results showed that Gerson Hall performed better than Rinker Hall in regard to natural ventilation potential Energy Usage Regarding energy usage, Rinker Hall, the LEED-certified building, performed better than Gerson Hall in both total annual energy usage and in energy use intensity (EUI). This was true in all three BEM programs (Figure 4-8). Ecotect simulations showed that Rinker Hall would consume less energy than Gerson Hall (56% difference between EUIs). Green Building Studio calculations also showed that Rinker Hall would consume less energy than Gerson Hall (20% difference between EUIs). Similarly, IES <VE> simulations estimated that Rinker Hall would consume less energy than Gerson Hall (36% difference between EUIs). Figure 4-8. Energy use intensity (EUI) comparison by building and by BEM tool. Dotted line denotes the CBECS national median EUI for educational building types (104 kbtu/sf) As of 2003, the CBECS national median energy use intensity for Education (College/University) building types is estimated to be 104 kbtu/sf. This serves as a 62

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

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

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

66 energy use values of the benchmark models. Ecotect simulations also showed that Gerson Hall (potential savings of 142,043 kwh) could possibly save more energy (35% difference) than Rinker Hall (potential savings of 92,516 kwh). IES<VE> was able to assess natural ventilation by providing average annual infiltration rates (cfm) for each zone. Gerson Hall had an average natural ventilation rate of CFM per square foot averaged over the entire inhabitable building floor area compared to Rinker s average natural ventilation rate of CFM per square foot. Thus Gerson Hall seemed to provide a 33% higher ventilaton rate than Rinker Hall. Table 4-2. Natural Ventilation Simulation Results for three BEM tools. Potential energy savings from natural ventilation (kwh) Ecotect Rinker Hall 92,516 Gerson Hall 142,043 Potential energy savings from natural ventilation (kwh) Green Building Rinker Hall 32,254 Studio Gerson Hall 57,883 Average CFM per square foot from natural ventilation IES<VE> Rinker Hall Gerson Hall The probable reason why Gerson Hall outperformed Rinker Hall based on natural ventilation results obtained by each of the three BEM tools, is Gerson Hall s orientation towards prevailing winds. Whereas Rinker Hall is oriented longitudinally north to south, Gerson Hall is oriented east to west (Figure 4-10). Prevailing winds in the summer months for these building locations come from the south-southwest. By exposing a larger surface area of the building to the prevailing winds (by orienting itself east to west), Gerson Hall has more interior rooms exposed to prevailing wind-assisted natural ventilation for times of year when natural ventilation is beneficial to reducing the cooling load. 66

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

68 subcriteria), and the criterion score for speed was calculated as x/6 for (six subcriteria). The highest possible score for each criteria was 1.00, and the highest possible comprehensive score was The cumulative scores used in the BEM tool re- evaluation were calculated by first converting the criteria scores into percentages. The final score (Σc ) was the sum of these percentages with each criterion receiving the same weight. The final score was calculated by equation [4-2]: Σc = c1+c2+c3+c4 [4-2] where: c1 = Interoperability; c1 = x/9, c2 = User Friendliness; c2 = x/11 c3 = Versatility; c3 = x/47, c4 = Speed; c4 = x/6, x = number of subcriteria supported by BEM tool for the respective criterion Figure Re-evaluation scoring system with criteria and subcriteria 68

69 Based on the un-weighted results from the re-evaluation, IES<VE> appeared to be the strongest of the three BEM tools used in the case study. This was largely due to IES <VE> receiving high marks in user-friendliness (score 0.73 out of 1.00) and versatility (0.91 out 1.00). Figure 4-12 illustrates the un-weighted comprehensive scores obtained by the re-evaluation. As mentioned, IES<VE> appeared to be the strongest with cumulative score of 2.75 out of 4 possible points. Green Building Studio had the second highest score of 2.41 out 4; and Ecotect had the lowest score of the three with a total of 2.14 out of 4. Figure Re-evaluation un-weighted cumulative scores A matrix was developed applying various weights to the criteria based on the user s order of importance. The criterion first in importance was multiplied by a factor of four, second by a factor of three, third by a factor of two, and fourth by a factor of one. This matrix yielded 24 possible combinations (Table 4-3). 69

70 Among the 24 possible weightings, IES <VE> achieved the highest score of 21. Based on the research findings Green Building Studio is recommended when speed is the highest priority for the user, and interoperability the second highest; and when the order of importance is speed, user-friendliness, interoperability, and versatility. The study recommends IES <VE> for any other combination of the criteria. Table 4-3. Re-evaluation matrix with various weightings Order of Importance Weight 1 Weight 2 Weight 3 Weight 4 Weight 5 Weight 6 Weight 7 Weight 8 Weight 9 Weight 10 Weight 11 Weight 12 Weight 13 Weight 14 Weight 15 Weight 16 Software selection Interoperability Userfriendliness Versatility Speed IES<VE> Interoperability Userfriendliness Speed Versatility IES<VE> Interoperability Versatility Userfriendliness Speed IES<VE> Interoperability Versatility Speed Userfriendliness IES<VE> Interoperability Speed Versatility Userfriendliness IES<VE> Interoperability Speed Userfriendliness Versatility IES<VE> Userfriendliness Interoperability Versatility Speed IES<VE> Userfriendliness Interoperability Speed Versatility IES<VE> Userfriendliness Versatility Interoperability Speed IES<VE> Userfriendliness Versatility Speed Interoperability IES<VE> Userfriendliness Speed Interoperability Versatility IES<VE> Userfriendliness Speed Versatility Interoperability IES<VE> Versatility Interoperability Userfriendliness Speed IES<VE> Versatility Interoperability Speed Userfriendliness IES<VE> Versatility Userfriendliness Interoperability Speed IES<VE> Versatility Userfriendliness Speed Interoperability IES<VE> 70

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

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

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

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

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

76 lowest score (0 out of 6). The major advantage of Green Building Studio in regard to this criterion had to do with its calculation engine being server based. Calculations were performed online which decreased calculation times in all three analyses types. IES<VE>, which had the second highest score was able to perform the three simulation types in under 1 hour. Ecotect, which had the lowest score for speed, had simulation times that lasted several hours. Table 4-7. Re-evaluation of three BEM tools for speed Subcriteria Ecotect Green Building Studio IES<VE> Energy simulation time under 1 hour Energy simulation time under minutes Daylighting simulation time under hour Daylighting simulation time under minutes Ventilation simulation time under hour Ventilation simulation time under 10 minutes Total Points (out of 6) Percentage score Guidelines for using Ecotect, Green Building Studio and IES<VE> During the case study, a log was maintained noting problems and observations for each of the three BEM tools used in the case study. The steps in the energy modeling process that were analyzed were model preparation in Revit, model preparation in BEM tool, weather data, energy analysis, daylighting analysis, ventilation analysis, and schedule implementation. The following section summarizes these observations (which are provided in full detail in the Appendix C) for each BEM tool. 76

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

78 Green Building Studio required the least amount of model preparation before running the initial simulation. The gbxml models of Rinker Hall and Gerson Hall exported from Revit were loaded directly into the Green Building Studio web-based analysis engine. In the online interface, users fill out a questionnaire about building type and location before the base simulation can run. After running a base simulation, iterations of the building model can be run by adjusting the building specifications in the project defaults tab. In this window, building specifications related to system types, constructions, and glazing are input. While this allows for simulations to run quickly and require minimal inputs, it limits the amount of editing a user may perform on the building model in Green Building Studio. Any changes to the building geometry and the interior organization of zones must be performed in Revit (or other gbxml-enabled BIM or 3D modeling platform). While gbxml models may be inspected using a third- party 3D model viewer, Green Building Studio is unable to edit potential building geometry errors that occur during the translation of the BIM model to gbxml file. Model editing proved to be useful in Ecotect and IES<VE> as many errors were found in the gbxml files. This capability is enhanced in both tools by including error detections. Ecotect s error detection occurs when the first simulation is initialized and provides a list of errors detected and corresponding location in the model (e.g. zone28, surface2093). IES<VE> performs its error detection when the gbxml file is imported. IES<VE> error reports also include corresponding locations in the model to the errors found. The most common error in both programs during the case study was errant holes in surfaces. Other major errors encountered in the gbxml files imported into the BEM tools were missing components, such as shade walls. Such components 78

79 were rebuilt in Ecotect and IES<VE>. Due to limitations of the software, these components were omitted from the Green Building Studio energy models. Like Green Building Studio, Ecotect and IES<VE> also may require to re-input envelope constructions. Both Ecotect and IES<VE> support a greater degree of versatility in specifying envelope constructions by allowing users to specify construction layers and layer properties. This is in contrast to Green Building Studio, which only allows users to specify construction types included in a drop down menu Weather Data Acquisition The proximity of weather data sources to actual building locations for the three BEM tools ranged from 4.0 miles to 0.8 miles (Figure 4-13). To obtain Gainesville weather data, the weather file for Ecotect had to be downloaded from the DOE EnergyPlus website. By comparison, Green Building Studio and IES<VE> had weather data libraries with Gainesville weather data already built into the software. Figure Location of weather data for three BEM tools in proximity to case study buildings 79

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

81 Figure Ecotect Schedule Editor Each of these schedules was implemented for all zones using adjusting the zone properties. Number of occupants and occupancy schedules were input under the tab occupancy. A generalized schedule assuming electrical lighting and room equipment run at the same time was input under the tab internal gains. The ventilation schedule was developed using the guidelines set forth by ASHRAE Standard (Figure 4-15). Under the tab infiltration rate, the study referred to the weather file to develop a natural ventilation schedule that was active for outdoor temperatures that fall within the ASHRAE Standard thermal comfort range. As per Standard , a wider comfort range is allowed when relying on natural ventilation. This meant that the ventilation schedule was developed so as to trigger the operable windows to be 100% open during the times of year when the outdoor temperature was within the acceptable comfort range for natural ventilation. Using the weather data provided by Ecotect, the schedule was developed by identifying those times of year and manually inputting them into the operable window schedule. 81

82 Figure Mean monthly average temperatures and corresponding comfort ranges. The shaded area refers to acceptable air-conditioned thermal comfort ranges, and the black lines refer to acceptable thermal range for natural ventilation. Dotted lines denote the acceptable thermal comfort range for given mean monthly outdoor temperatures (ASHRAE 2004). Green Building Studio : The only schedule that could be implemented into the energy models in Green Building Studio was the occupancy schedule. The option School, year-round was selected from a drop down menu during the initial questionnaire when the gbxml file was initially imported into Green Building Studio. Users are unable to create their own schedules, or adjust occupancy schedules in the drop down menu. For this reason, Green Building Studio is not recommended for calibration purposes. IES<VE> : Schedule is handled in the Apache module with the icon for Apache profile database manager. Each room has been assigned a profile from a drop down menu in the ModelIT module. These can then be customized by editing the profiles in Apache. Users may create their own schedules here as well allowing for a degree of customization (Figure 4-16). This allows users to input values in the schedule either graphically or numerically. 82

83 Figure IES<VE> schedule editor interface This was especially useful when developing operational profiles for operable windows. Unlike Ecotect, which required users to develop daily schedules based on climate data, IES<VE> s schedule (profile) editor allows users to devise schedule based on formulas as well using the modulating formula profile creation tool (Figure 4-17). In this way, the operable window schedule was input by triggering operable windows to open based on thermal parameters. These were input as temperature ranges derived from ASHRAE Standard Operable windows were open 100% when the outdoor temperature was less than 78 F and greater than 70 F Energy Analysis Each BEM tool reported energy usage in different ways and had varying ranges of capabilities. The extent to which users are able to customize reports and energy analyses varied as well. Green Building Studio, which was the quickest to generate energy reports, was limited in output options. Ecotect and IES<VE> provided more versatility in outputs, but had longer calculation times (under one hour calculation times for IES<VE> while Ecotect calculations could take several hours). In particular, 83

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

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

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

87 data in analysis graphs and reports. The analysis grid is helpful for users to locate areas in zones that do not have adequate daylighting (2% daylight factor by LEED standards), and provides graphical cues as to where to place electrical lighting efficiently, and alternative glazing strategies to improve daylighting performance. One disadvantage of Ecotect s daylighting simulation engine is that it was not uncommon for simulation runs to take several hours. Using simplified models can reduce the calculation time. However, it was noted that reducing the complexity of the gbxml file export from Revit led to more errors in the model upon importing it into Ecotect. Green Building Studio : Glazing Factor (inverse of daylight factor) is the parameter that Green Building Studio uses to assess daylighting performance. Results are broken down on a zonal basis. Green Building Studio does not allow the user to specify sensor positions in the model. This is a major disadvantage for users simulating daylighting performance for specific areas within zones (e.g. the location of a desk). Users are also unable to specify the date and time of the simulation run. Without any of this information, it is difficult to utilize a single simulation run s daylighting data. These reports are useful to compare design alternatives. While daylighting analysis in Green Building Studio is not very versatile, simulation runs are much quicker, only taking a matter of seconds (dependent on user bandwidth). The daylighting results are also tailored to show effectiveness of the building s daylight performance compared to the requirements for LEED credits. These credits are awarded if the building is able to provide a glazing factor of 0.02 for at least 75% of the regularly occupied floor area. IES<VE> : Daylighting was performed using the FlucsDL module in IES<VE>. Prior to running the FlucsDL simulation, the SunCast module was used to update the 87

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

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

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

91 building performance may find Green Building Studio more favorable; meanwhile users requiring more detailed analysis and control over how data is displayed will find Ecotect and IES<VE> more suitable. 4.5 Guidelines for Using Building Energy Modeling The following sections provide guidelines and recommendations for selecting and using BEM for the analysis of high performance buildings. Section provides guidelines for utilizing BEM and provides recommendations for BEM application in various phases of the building lifecycle. Section provides potential BEM users with guidelines for selecting the appropriate BEM tool. Intended users of the guidelines are beginner energy modelers. These may include building designers and green building consultants. The guidelines are based on observations made during the case study. As such, the guidelines are tailored to the energy modeling methodology used in the research as illustrated in Figure Figure Workflow of energy modeling methodology employed in case study Potential BEM users are encouraged to use the guidelines as a template for developing their own energy modeling methodology and energy modeling software criteria for evaluation and selection. Adaptations to the energy modeling methodology 91

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

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

94 extent to which each BEM tool is able to supports the recommended capabilities is indicative of how useful the BEM tool is for each respective building lifecycle phase. Table 4-8 identifies capabilities that are useful during the conceptual design phase. All three BEM tools appeared useful for the conceptual design phase with each tool supporting ten out of the eleven recommended capabilities. Table 4-8. BEM tool use during conceptual design phase BEM Capability Ecotect Green Building Studio IES<VE> Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Building geometry creation X X Orientation X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system selection X X X Design alternative assistance X Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X. Table 4-9 identifies capabilities that are useful during the design development phase. All three BEM tools appeared useful for design development with Ecotect and IES<VE> supporting all ten of the recommended capabilities and Green Building Studio supported nine out of the ten. Table 4-9. BEM tool use during design development phase BEM Capability Ecotect Green Building Studio IES<VE> Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system refinement X X X Resource management X X Lifecycle cost analysis X X X 94

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

96 Table 4-12 identifies capabilities that are useful during the facilities management (building operation) building lifecycle phase. IES<VE> appeared to be the most useful BEM tool for this building lifecycle phase supporting five out of the five recommended capabilities. Ecotect was the second most useful supporting three out of the five functions, and Green Building Studio was the least useful supporting none of the four. Table BEM tool use during facilities management phase BEM Capability Ecotect Green Building Studio IES<VE> Model calibration (operational profiles) X X Model calibration with plant data X Energy and cost benefits for changes to X X lighting systems Energy and cost benefits for changes in X HVAC system operation Energy and cost benefits for building envelope chagnes X X Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X. Based on the capabilities provided by each BEM tool, tables 4-8 through 4-12 suggest that Ecotect and IES<VE> are useful BEM tools from conceptual design phase to facilities management phase, while Green Building Studio is recommended for use in early design stages (conceptual design and design development). Based on Ecotect s capabilities, it appeared useful from the conceptual design phase through facilities management. Green Building Studio appeared to be useful primarily in early design stages (conceptual design and design development), but with limited applicability to more detailed design stages, construction phases, and facilities management. This is largely due to Green Building Studio having limited versatility in inputs and outputs. These limitations make it very difficult to calibrate energy models. IES<VE> s capabilities appeared useful for all building lifecycle phases from conceptual design to 96

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

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

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

100 research. For example, the criterion of accuracy was not included in the scope of this research, but may be an important criterion for potential guidelines users. Figure Guidelines for BEM software selection 100

101 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS By investigating existing BEM tools, the research provided insight on use of energy modeling, both in terms of practice and capabilities. In practice, the integration of BEM into building design, construction, and facilities management (still in development) will almost certainly lead to smarter, and increasingly energy efficient buildings. However, it remains to be seen how well these BEM tools perform for measurement purposes. Until then, the capabilities of BEM tools are limited in application. The study recommends such BEM tools for use primarily in design. The energy model may be used in an iterative workflow to improve energy efficiency against a baseline model and cautions users relying on BEM software to predict actual energy performance. 5.1 Conclusions The following section summarizes the conclusions made during the research and is broken down based on the initial objectives of the research Objective 1: Initial Evaluation Based on the literature review four major criteria were identified to evaluate 12 major BEM software. These criteria were interoperability, user-friendliness, available inputs and available outputs. Based on these four criteria for evaluation, the study identified Autodesk Ecotect, Autodesk Green Building Studio, and IES<VE> as the top three out of the twelve evaluated Objective 2: Case Study The case study used the top three software (Ecotect, Green Building Studio, and IES<VE> ) to compare the environmental performance of Rinker Hall (LEED Gold 101

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

103 on calculation speed. However, for most other criteria orders of importance, the study recommends IES<VE> Objective 4: Developing Guidelines for Using Building Energy Modeling Intended users of the guidelines are building designers and green building consultants. The guidelines were tailored to aid in BEM selection and application for specified building lifecycle phases. Based on the required BEM capabilities for each building lifecycle phase, it was evident that many of the BEM tools investigated in the study are appropriate for early design stages, while only a few (IES<VE>, Ecotect, and equest ) may be useful for later design phases, construction and contracting, and facilities management. 5.2 Research Limitations As an evaluation of existing BEM tools, the research sought to develop a methodology that compared these tools in a relatively consistent manner. This proved to be very difficult as the existing BEM tools are very diverse with different intended users and applications. Thus, while the research attempted to develop criteria for evaluation that could fairly compare such diverse programs, these criteria are almost certainly tailored to a preconceived notion of BEM while the project was still in its developmental stage Objective 1: Initial Evaluation The initial cross evaluation relied on information gathered during the literature review to fill out sub-criteria checklists for each criterion for evaluation. The data was limited to available data and literature to fill out these checklists. Ideally, the study would have test driven each of the 12 BEM tools used in this portion of the study but was limited by time and software costs. This portion of the study also assumed an even 103

104 weight applied to each criterion for evaluation. In order to select the top three BEM tools out of the 12 investigated, the study was limited at this portion to an even-weighted scoring system Objective 2: Case Study In the development of any energy model, a number of assumptions must be made. The number of variables that affect building energy usage are vast, so the model is reliant on a number of assumptions and conditions. These assumptions also varied from program to program based on the available inputs provided for each one. In all scenarios, the implementation of schedules is always an approximation as it is impossible to predict the actual behavior of occupants and building operation practices. Ecotect and IES<VE> have capabilities of implementing increasingly accurate schedules that could be customized on a zone-by-zone basis. Meanwhile, generalized assumptions were made in Green Building Studio about occupancy and operation based on default values and averages for schedules for higher education building types. Similarly, values for lighting power density and equipment power density were based on standard and averaged values per building type based on the ASHRAE 90.1 Standard (this was applied to all three BEM tools). These values along with corresponding schedules simulate approximations in regards to HVAC use and internal gains. Regarding daylighting performance (as per LEED requirements) CIE uniform sky conditions for simulation purposes were assumed in all three BEM tools. Ecotect s daylighting calculations were limited to only taking daylight factor data for December 21 (worst case scenario), while IES<VE> s daylighting simulations were limited to September 21 (average case scenario). Green Building Studio s daylighting simulation 104

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

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

107 the same problems, or discover other problems, etc). The guidelines presented in the research are thus based on a single energy modeling methodology and workflow. 5.3 Recommendations for Future Research As Krygiel and Nies (2008) note, the two primary ways in which BEM tools are utilized are for design and for measurement. While this research can remark on the applicability of BEM as a design tool and for meeting simulation-based LEED credit requirements, the accuracy of these BEM tools remains to be assessed. In that regard, these tools are limited to acting only as design tools and for the sole function of improving environmental performance. Future research assessing the accuracy of these BEM tools, particularly those used in the case study, could be useful to provide recommendations to software developers, and could potentially improve the faith in BEM users that buildings will meet intended performance requirements. In particular, future research could focus on measuring simulated energy usage against measured data for each of the two buildings used in the case study and compare energy use breakdowns. System levels and operational profiles (schedules) can be adjusted to calibrate the energy models with actual building operation. Another objective of future research could be a comparison of gbxml file-based energy models and IFC file-based energy models. As several model errors were discovered in the interoperability between Revit and the BEM tools via gbxml file import/export, it would be useful to BEM users to gain insight into which data schema contains less model errors. A couple of changes in the research methodology would be made if the study were to be conducted again. For one, the more comprehensive criteria for evaluation used in the re-evaluation would also be applied to the initial evaluation. As the research 107

108 progressed through the case study phase, the criteria for evaluation became more refined. Secondly, the study would have compared the daylighting performance for all regularly occupied spaces of the two buildings as opposed to selected rooms. In this way, a more accurate and comprehensive comparison of the daylighting performance of the two buildings could be made. Finally, the criterion of accuracy should be added to the re-evaluation of the BEM tools. The objective of the future research will be to validate the accuracy of the BEM tools. An additional study comparing the data of simulated energy usage against measured data for the two buildings used in the case study is recommended for future research. The percent differences between simulated data and measured data could serve as the basis for scoring the BEM tools in the accuracy criterion, and these scores can be added to those in the re-evaluation as an additional criterion for evaluation. 108

109 APPENDIX A INITIAL EVALUATION 109

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

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

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

113 Table A-3. Continued 113

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

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

116 APPENDIX B CASE STUDY 116

117 Table B-1. Annual Energy Usage Rinker Hall (output of Green Building Studio simulation) Energy, Carbon and Cost Summary Annual Energy Cost $90,956 Lifecycle Cost $1,238,824 Annual CO2 Emissions Electric tons Onsite Fuel 49.9 tons Large SUV Equivalent 45.8 SUVs / Year Annual Energy Energy Use Intensity (EUI) 59 kbtu / ft² / year Electric 687,488 kwh Fuel 8,601 Therms Annual Peak Demand kw Lifecycle Energy Electric 20,624,649 kw Fuel 258,015 Therms Figure B-1. Rinker Hall energy use breakdown (output of Green Building Studio simulation) 117

118 Figure B-2. Rinker Hall annual fuel use breakdown (output of Green Building Studio simulation) Table B-2. Annual Energy Usage Gerson Hall (output of Green Building Studio simulation) Energy, Carbon and Cost Summary Annual Energy Cost $87,013 Lifecycle Cost $1,185,112 Annual CO2 Emissions Electric tons Onsite Fuel 43.2 tons Large SUV Equivalent 44.0 SUVs / Year Annual Energy Energy Use Intensity (EUI) 78 kbtu / ft² / year Electric 667,753 kwh Fuel 7,443 Therms Annual Peak Demand kw Lifecycle Energy Electric 20,032,602 kw Fuel 223,282 Therms 118

119 Figure B-3. Gerson Hall energy use breakdown (output of Green Building Studio simulation) Figure B-4. Gerson Hall Energy Use Breakdown (output of Green Building Studio simulation) 119

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

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

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

123 Table B-7. Natural Ventilation Airflow Rinker Hall (output of IES<VE> simulation) Rinker Hall Room Designation Sq. Ft. avg. CFM 30 mech A elec Medium Classroom Large classroom A Elec Student lounge MEP Studio shower shop soils/conc structures studio A Stroage Interview Interview Men Women A Mech Room Tech DES A Server Room Plan Room Janitor Computer Lab A Storage Information Tech MCE Medium Classroom Medium Classroom Medium Classroom Medium Classroom Medium Classroom Storage A Elec Construction Est/Dwg/Sch Men A Storage Women A Storage Admin Director Grad Main Conference Room Main Office

124 Table B-7. Continued Rinker Hall Room Designation Sq. Ft. avg. CFM 305A Office Mgr Director Associate Director Storage Faculty Office Mail/Kit/Copy Faculty Office Conference Room Resource Center Faculty Office Faculty Office Faculty Office Faculty Office Grad Studio Faculty Office Faculty Office Faculty Office Grad Studio Faculty Office Grad Studio Faculty Office Grad Studio Faculty Office Faculty Office Faculty Office Closet BCIAC B Elect Closet CPR CCE CCSLC Endowed Chair Storage E-Journal Editor Men A Janitor Women C199D Corridor C299D Corridor

125 Table B-8. Natural Ventilation Airflow Gerson Hall (output of IES<VE> simulation) Gerson Hall Room Designation Sq. Ft. avg. CFM 103 Elec Janitor Men Women Data/Comm mechanical B Elec C Fire Pump MACC Services Gallery Student Office Medium Classroom Medium Classroom Control Room Teaching Assistants Large Classroom Men Women Janitor Men Women Data/Comm Mail Clerk Clerk Stor/Admin Support Small Conference Director / Chair Asst Dir Dept Chair Gen Staff Coord Work Room M Acc Reading Room Small Classroom Small Classroom Break - Out Break - Out Break - Out Break - Out Break - Out Break - Out Break - Out Break - Out Break - Out Storage Elec Janitor Men Women Data / Comm Office Office

126 Table B-8. Continued Gerson Hall Room Designation Sq. Ft. avg. CFM 311 Office Office Office Office Office PhD Office Office Office Office Office Office Office Conf Rm Support Large Conference Room Faculty Reading / Lounge A Faculty Support PhD Office Office Office Office Office Ph D Office A Stor Office Office Office Office Office C199A Commons Area C199C Corridor C199G Entry/Corridor C199H Corridor C199J Corridor C299A Corridor C299B Corridor C299C Corridor C299F1 Corridor C299F Corridor C299G Corridor C399A Corridor C399C Corridor C399D Corridor

127 APPENDIX C GUIDELINES FOR USING BUILDING ENERGY MODELING 127

128 Table C-1. Ecotect Guidelines and Recommendations Matrix 128

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