Sustain: An experimental test bed for building energy simulation

Energy and Buildings 58 (2013) 44–57

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Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild

Sustain: An experimental test bed for building energy simulation Donald Greenberg a,b,d,∗ , Kevin Pratt c,d , Brandon Hencey e , Nathaniel Jones b , Lars Schumann b , Justin Dobbs e , Zhao Dong b , David Bosworth b , Bruce Walter b a

Computer Science, Architecture, Johnson Graduate School of Management, Cornell University, United States Program of Computer Graphics, Cornell University, United States Architecture, Cornell University, United States d David R. Atkinson Center for a Sustainable Future, Cornell University, United States e Mechanical Engineering, Cornell University, United States b c

a r t i c l e

i n f o

Article history: Received 11 July 2012 Received in revised form 12 November 2012 Accepted 17 November 2012 Key words: Software applications Building energy modeling Building energy simulation Whole building energy analysis

a b s t r a c t Current building energy simulation technology requires extensive labor, time and expertise to create building energy models, substantial computational time for accurate simulations, and generates data in formats that make results difficult to interpret. These deficiencies can be ameliorated using modern graphical user interfaces and algorithms which take advantage of modern computer architectures and display capabilities. This paper describes a novel test bed environment which offers an interactive graphical interface, provides access to simulation modules that run at accelerated computational speeds, and presents new graphic visualization methods for the interpretation of simulation results. Its modular structure makes it suitable for use in early stage building design, for use as a research platform for the investigation of new simulation methods, and for use as a tool for teaching concepts of sustainable design. Improvements in the accuracy and execution speed of many of the simulation modules are based on the modification of advanced computer graphics rendering algorithms. Significant performance improvements are illustrated in several computationally expensive energy simulation modules. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The use of building performance simulation in architectural design processes is hindered by three key bottlenecks – the significant time and skill required to create building models for energy simulations, the time required to compute accurate simulations for geometrically complex models, and the difficulty of understanding and visualizing the results. Thus, in current practice, the creation of models for Building Energy Simulations (BES) has relied on model simplification in order to speed simulation time and reduce the effort required to augment the geometric model with the additional meta-data necessary for simulation. Conventional methods of producing thermal models through extrusion of prismatic building elements are reinforced by currently available thermal modeling software interfaces such as Ecotect [1], eQuest [2], IES-VE [3], DesignBuilder [4], and OpenStudio [5]. Furthermore, simulation engines such as EnergyPlus [6] and DOE2 [7] exploit these simplified space volumes to decrease simulation time.

∗ Corresponding author at: Computer Science, Architecture, Johnson Graduate School of Management, Program of Computer Graphics, Cornell University, United States. Tel.: +1 607 255 7444; fax: +1 607 255 0806. E-mail address: [email protected] (D. Greenberg). 0378-7788/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2012.11.026

In contrast, today it is normal for architects to design more complicated buildings using complex polygonal geometries or parametric surfaces with intricate shading devices, all of which affect the solar shading and radiative transfer properties of external surfaces. Thus the inability to deal with the complexity of current architectural designs has made the process of model simplification even more difficult. For these reasons, current methods involve either remodeling buildings specifically for energy analysis, entering dimensional data describing building geometry into a numeric interface, or manually transforming surfaces described as triangulated meshes or spline surfaces [8,9] into hierarchical polygon surfaces. The ability of the architect to simulate early-stage design concepts is particularly hindered by this vestigial habit of geometry manipulation. Because these methods for converting an architectural model into a thermal model are time consuming, tedious, error-prone, and expensive, numerical simulation is postponed. As a result, many buildings are only simulated late in the design process by consulting engineers. The three key bottlenecks described in the introductory paragraph can be understood as three distinct but interrelated problems. The first – the difficulty in making models amenable to analysis – is an input problem. The second – that simulation times are excessive – is a computer processing problem. The third

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Fig. 1. Sustain interface: a three-panel graphical user interface for data input (left), simulation output (right), and simulation control (center).

– the difficulty in understanding the simulation results – is a visualization problem. To advance the goal of creating an integrated process for sustainable design, we have developed a graphically oriented design and energy analysis test bed that takes advantage of next generation computing environments. We call this prototype Sustain. Our goal in creating Sustain was to design a software environment that would allow each of these key bottlenecks to be studied, potential solutions to be refined, and ultimately integrated into our test bed. To that end, Sustain is a software framework that can be used to perform building energy analysis, a research platform that enables the investigation of modern computational methods for accelerating building simulations, and a teaching tool for sustainable building design. Because the first and third bottlenecks are inherently humancomputer interface problems, all input and output modules of Sustain employ graphical user interfaces that provide real time user feedback. An example of the input/output environment is shown on a three-screen display in Fig. 1. The second bottleneck – slow simulation – arises primarily from the fact that many of the algorithms currently used in building simulation date from a time when computers had limited processing power and relatively small amounts of random access memory. Consequently, many of these algorithms do not take advantage of recent advances in both hardware and software architecture. The Sustain framework allows us to take advantage of these advances by providing access to parallel and heterogeneous computing architectures, deep hardware pipelines, and modern programming environments. The system currently runs on multiple modern workstations, some with advanced graphic accelerators, all connected to a high bandwidth network and a parallel compute cluster. To date, this environment has enabled the development of simulation modules that execute orders of magnitude faster than currently available software. The Sustain platform is also designed to be modeling tool and simulation engine independent. It can be modified to interact with a variety of modeling or simulation programs. Currently Sustain uses SketchUp [10] as its primary geometric modeler, although models from 3DSmax [11], Rhinoceros [12] and Grasshopper [13] have already been utilized. To date, EnergyPlus has served as its primary simulation engine, but other types of simulations such as Radiance [14] and custom resistor-capacitor network [15] thermal models can also be executed. The advantages of a fast, easy-to-use graphical interactive energy simulation system are many. Accelerated computation, when married with a graphical user interface, will allow analytical feedback at the early stages of design when informed decision making can have the greatest long term effect. It will foster collaboration between architects and engineers by making the consequences of formal decisions evident at the time such decisions are being made.

It will provide comprehensive visual feedback and thus reduce the potential for critical errors and omissions. It will enable building design teams to use accurate methods for predicting energy usage and carbon emissions at the conceptual design phase. In the future, it may allow regulators to include performative evaluations in modern energy codes. With easily understood graphical input and output, the system can ultimately be used for teaching sustainable design courses for architects and engineers. Thus, it is our hope that Sustain will serve as a prototype for the next generation of building design and energy analysis software. This paper describes the concepts behind the Sustain test bed software, our current hardware platform and presents examples of research conducted using Sustain as a development environment.

2. Sustain 2.1. Goals Our initial goals were to create a framework to support three primary user groups: designers, researchers, and teachers. • For building designers and simulation practitioners, Sustain is able to read geometry directly from CAD programs via a plugin and manage various simulation engines. Currently Sustain supports EnergyPlus for thermal simulation and Radiance for daylight simulation. Users can interactively manipulate design and simulation parameters within Sustain’s common graphic interface. Results are displayed graphically and associated with building geometry so that thermally inefficient assemblies can be identified and modified early in the design process. • For researchers, Sustain offers modularity which allows new simulation tools to be implemented and tested quickly. Because geometric translation is handled internally, a rich variety of building models can be rapidly created and tested. Results from any module are available to any other module, which means that they may serve as input to other simulations (as in pre- or postprocessing) or for visualization. Custom visualizations can also be created using 2D and 3D libraries. • For teaching environments, Sustain provides a fast and intuitive laboratory for illustrating the effects design decisions have on building performance. Students may quickly alter material, climate, and simulation parameters through graphical interfaces. Changes to building geometry in the CAD program are immediately reflected in Sustain and available for simulation. The graphical display of the results allows for fast and intuitive comparison of design alternatives.

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As a first step, we evaluated the building energy modeling (BEM) and building energy simulation (BES) programs which were available. We then evaluated the strengths and weaknesses of the software according to the criteria we deemed important to meet our goals. We chose EnergyPlus as the BES simulation engine because of its well organized modular structure and the fact that the software was designed to facilitate adding features and links to other programs. Since our initial research efforts were to accelerate simulation computations with new modules, this was a major factor. EnergyPlus also had other attributes which were important to our research. The first is that their approach provided an integrated simulation, an important characteristic for the accuracy of the heat balance equations [16]. Although the approach has some simplifying assumptions, such as a uniform temperature distribution and one dimensional heat-flow on all surfaces, and also presumes a “well stirred” air environment, it appeared to be the most accurate calculation method unless one executes a full computational fluid dynamics (CFD) simulation. The second major characteristic is that it has variable time steps so that one can easily change time increments based on the need relative to the stage of the building design. Both of these characteristics should help in allowing our simulations to transit from approximate solutions at early stage design to more accurate results at late stage design. EnergyPlus also simulates HVAC systems, an area which we are not yet investigating. 2.2. Overview The Sustain framework depends on several important components: • The development of a comprehensive graphical user interface to reduce the difficulty of data input tasks. • The creation of a semi-automated translation method for converting geometric architectural models to simulation compliant models, simplifying the process of energy model creation. • The development and modification of advanced computer graphics algorithms to replace specific computationally expensive modules used in energy simulations, thereby reducing the total simulation time. • The ability to execute multiple simultaneous simulations in parallel on clusters of multi-core computers, enabling parametric studies and multiple model comparisons. • The creation of new visualization methods for displaying the results of energy simulations so that designers can easily interpret the data, rectify errors, and modify their designs. Our prototype system takes particular advantage of the major advances in hardware and software which have evolved in computer graphics during the last three decades. Past research in computer graphics has resulted in sophisticated algorithms, optimized software, and deep hardware pipelines, and has improved rendering speeds by four orders of magnitude since the time of their original inception. What is not generally realized is that the algorithms used for photorealistic rendering and some of the key algorithms used in energy simulation are similar [17,18]. Both types of analysis are concerned with the propagation of energy and the effects of material properties on the nature of the dynamic relationship between matter and energy in space and time. These algorithmic techniques used in rendering can be directly applied to shadowing calculations, visibility analyses and radiant energy exchanges between building components. Graphics processing units and texture mapping hardware can help speed the calculations. Methods similar to “environment mapping” [19,20] can be applied to determine the thermal impact of neighboring buildings

and adjacent surfaces, particularly important in urban settings. Parametric curves and surfaces [21] can be used to store data results and thus accelerate computations for energy calculations without loss of accuracy by reducing sampling requirements. Additional opportunities to reduce the simulation computation time arise from the massive change in computing environments that allow for the parallelization of complex algorithms. We are just entering the fourth generation of computing characterized by “cloud computing,” [22] and broadband networks with high bandwidth interconnections. As we move to the process technologies of the next generation computing, we are witnessing an increase in parallel computing and memory storage capacity of local devices. By efficiently utilizing this shift to multiple core architectures through code level and program level parallelization, computation times for effective energy simulations can be significantly reduced. 2.3. Sustain software framework This section describes the conceptual software organization of the Sustain framework. Fig. 2 presents an overview of the sequential operations necessary to convert data from a CAD model (green) to a BEM model (blue) to a BES simulation execution (pink), accessed through a graphical user interface (yellow) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.). An external (CAD) modeling system is used to generate the building and site geometry. This data is augmented with additional material and assembly data (if necessary) and weather information, all through the graphical user interface. The CAD data is combined with thermal metadata through a translation process to create a building energy model. Multiple simulations using different simulation algorithms or different building geometries can be executed in parallel, all under the control of a simulation manager. All output results can then be displayed visually as shown in the Sustain Interface (Fig. 1). More detailed explanations of the individual modules are described in Sections 3, 4, and 5. The basic iterative work flow supported by Sustain can be described as a series of discrete steps: 1. The creation of a digital geometric model of a building or group of buildings and its surrounding environment. 2. Augmenting the geometric model with the associated material data. 3. Assigning a physical location and appropriate climatic data to the geometric model. 4. Conversion of the geometric model and associated metadata to a form that is amenable to analysis. 5. Visualization of the model in relation to both its location and specific climate data. 6. Execution of simulation(s). 7. Analysis and visualization of simulation results. 8. Modification of either the geometric model or the associated metadata. 9. Re-simulation and re-visualization (repeat as necessary). The Sustain software framework is capable of handling tasks two through nine. It depends on an external modeling environment for the actual creation of the digital building geometry (step one). It uses plug-in interfaces to allow for (but does not require) the association of necessary metadata inside the modeling environment, thus steps two and three can be performed in either the modeling interface or the Sustain interface. The conversion of the geometric model to a thermal model (step four) is accomplished by extracting a minimal amount of data from the modeling environment via a plug-in, and then processing that data within the Sustain environment [23]. The visualization of the model

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Fig. 2. Sustain software framework: input geometric (CAD) data and metadata from external sources are converted to a thermal model (BEM) and made available for simulation execution (BES).

and its metadata (step 5) is accomplished by the Sustain graphical interface. The interface also enables the initiation of various simulation modules (step 6), some of which are prototypes and are contained within the Sustain codebase, and some of which are simulation engines developed by third parties and are called and controlled by Sustain. Some of the third party simulation engines called by Sustain (such as EnergyPlus) have been modified to take advantage of preprocessing routines offered by the Sustain environment. The visualization and analysis of the simulation results is accomplished via the Sustain graphical interface, which provides an extensive suite of visualization tools and access to programming tools that allow for the creation of new visualization methods. Once the model has been translated into an object-oriented BEM, the metadata associated with the model can be changed without reinitiating the model transfer protocol. Thus material data and climate data can be modified without recourse to the modeling environment. If the user wishes to change the geometry of the model, this is done from within the external CAD geometric modeling program and the new model must then be translated to the Sustain environment. At the heart of Sustain is a data structure that stores building and environmental information in active memory and makes it available to peripheral modules that perform simulation and visualization. We use object-oriented Java routines so that the platform may be easily expanded with new simulation modules. Graphic hardware pipelines are utilized in some of our routines for simulation acceleration. Advantage is taken of the parallel capabilities provided by modern networking through compute clusters. 2.4. Current Sustain hardware configuration Sustain was designed to run in a modern graphics workstation environment with large storage capacity, a dedicated parallel compute cluster, and high bandwidth communications between all

components. Modern workstations have central processing units, each with multiple cores. Each core can run tasks at the same time in parallel without any performance penalty. Our compute cluster consists of 32 nodes, (blades) each with eight cores and could have access to cloud computing. Although many simulators do not support parallel computing, we bridge this gap by implementing a very flexible simulation management system (Fig. 3). We introduced a head node to our compute system, so that multiple workstations running Sustain can access the cluster at the same time. The head node runs a job queuing server. Sustain only notifies the job queue that a building needs to be simulated, but does not wait for the simulation to finish. To avoid the case of multiple users simulating the same model, it gets flagged as being queued. The head node checks which nodes are not used to their full capacity and when a node is available, starts the simulation on that node, and collects the data at the end. Each node can run multiple simulations depending on how many cores are available. We allow for a heterogeneous cluster, meaning that the nodes can have different speeds or number of cores. The head node uses zero configuration networking to automatically create a usable computational cluster without a predetermined configuration or intervention by a system administrator. The head node also runs a service discovery to find active cluster nodes and after discovery automatically queries how many cores exist in each node, how to connect to the nodes, and who is running what on the nodes. Nodes can be added or removed anytime and the system reconfigures automatically. Each node only runs a small program allowing the execution of remote commands. The major advantage of this flexible software/hardware system is that it allows multiple users to run many simulations at the same time, providing the opportunity for parametric studies and comparison of designs as well as a collaborative workspace. Actual execution time could be greatly accelerated if more portions of the

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Fig. 3. Simulations are managed over a network in which computers running Sustain request a simulator from a head node.

simulation code (EnergyPlus) could run in parallel, but we have not attempted to approach this formidable task. 3. CAD modeling Many CAD modeling tools [10–12] are polygon- or spline surface-based, are easy to use, and can produce complex geometric data. Not surprisingly, much of this software uses geometry and material description algorithms originally designed for the entertainment industry to create exquisite and believable imagery. However, the data such software produces is not suitable for energy simulations. To use these CAD systems for energy modeling it is frequently necessary to both transform geometry and augment the geometric descriptions with additional information. We have created interactive graphics routines that enable the input of location and material assembly data (Fig. 2) to supplement the CAD model data after its translation to BEM data. These are described below. We have also developed an input pipeline that manages the geometric translation and adds the hierarchical structures and object level relationships necessary for thermal simulation (Section 4.1). 3.1. Location, climate and terrain data Free applications such as GoogleEarth [24] and World Wind [25] have already demonstrated the ease with which GIS data can be made available in an intuitive graphic format. We are able to interactively select a building’s site and automatically extract relevant environmental and meteorological data from online, publically available databases (Fig. 4a and b). Intuitive graphic climate visualization modules embedded in Sustain’s graphical interface, including time-dependent temperature, wind direction, light levels, and CIE sky conditions [26], provide the designer with immediate graphical feedback. Once the site location is specified, we can extract both topographic information and climate data for simulation. By automatically extracting terrain and weather data from the internet, the designer is empowered to work with real measured site data, rather than starting from the tabula rasa of a blank screen (Fig. 4c). Upon export to Sustain, the terrain data is understood as a possible shading device, so the simulations can account for the shading effects of the topography.

thickness of material layers by dragging them with the cursor and see live updated thermal and environmental impact properties such as R-value and solar transmittance. The editor can open and save materials and assemblies to multiple sets of databases. Sustain differentiates between materials, which have intrinsic thermal and visual properties, and assemblies, which are spatially-organized collections of materials. Material and assembly databases are stored as text files. The material database stores properties for each defined material, and the assembly database stores lists of materials along with corresponding thicknesses that make up defined construction types. The flexible text-based nature of the material database makes it easily extensible. In our database columns represent one type of physical property, while each row stores a specific material. Any new material property required that is not already present in the database can be added with the insertion of new columns. Currently, the database includes properties for material rendering by CAD systems and for thermal analysis. In the future, we plan on including data for new thermal, structural, acoustic, optical, lighting, and rendering properties. It may also include data from material database software such as CES selector [27] and be able to export assembly data to life cycle analysis software as Athena Impact Estimator for Buildings [28]. 4. Building energy modeling (BEM) module By far the most time consuming and tedious task in building energy simulation is the creation of BEM models for testing. This is not without irony because often the architect has already produced a complete yet incompatible building model by the time the first simulations are requested. In order to receive prompt feedback on the thermal performance of a building, architects need a tool for accurate automated translation of a model into a thermal form compatible with a simulation engine. With Sustain, we bypass this bottleneck in the simulation process by introducing a semiautomated pipeline to translate geometric building data, augment it with material data, and generate necessary hierarchical relationships between building components. Graphic controls allow the user to initiate this import process and with, graphical feedback, to manually adjust the imported model as desired. 4.1. Translation

3.2. Wall section, material and assembly data Sustain’s wall section editor allows architects to create custom wall, window, floor, and roof assemblies using an intuitive interface (Fig. 4d). Designers can interactively adjust the position and

Most CAD modeling programs are surface-based and offer a set of properties that can be applied to individual surfaces. We created small plug-in programs or scripts for different CAD modeling programs that convert geometry and surface properties to a uniform

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Fig. 4. Model Input Panel.

format understood by Sustain [23]. We use this process to export five surface properties that can be found in essentially any CAD modeling program: layer, material, vertices, vertex normal vectors, and texture coordinates. The layer may be used to enforce userdefined thermal zoning, although a semi-automated routine for thermal zone creation is under development. Materials, imported from an assembly database ensure that surface appearance in the CAD program is similar to that in Sustain and allow the user to specify material thermal characteristics. The geometric boundaries of each planar surface polygon in the model are defined by vertices. Vertex normal vectors and texture coordinates provide sub-surface detail on curvature, transparency, and other factors. These last two attributes are often used for photo-realistic rendering, and their integration into BES software will allow modeling of more complex buildings. For all of the above to work in a seamless fashion, a semiautomated system interprets geometric models created in the CAD environments and prepares this data for export to BES software (Fig. 5). This system integrates four types of processes that act on the geometry and other data available from the CAD program. These include ensuring geometric compatibility, the attachment of thermal properties to each geometric element, and the identification of all surfaces defining the building’s exterior envelope. At the conclusion of the automated translation, Sustain displays the zone decompositions graphically on the model and allows the user to check and alter the heuristic solutions. The first necessary step is generating geometric compatibility. The raw data output by CAD program plug-ins often includes extraneous or duplicated information. Thus, a clean-up step is necessary. Additionally BES tools such as EnergyPlus contain built-in geometric assumptions. The geometric description, but not the geometry itself, may be modified to be compatible with EnergyPlus without tedious user interaction. For example, in this step degenerate polygons and polygons with near-zero area are removed from the

model, co-linear vertices are removed from polygons, vertex ordering is adjusted and adjacent edges of surfaces are “welded”. The second step is a set of heuristics that determine thermal properties of surfaces based solely on information obtained about each surface from the CAD program. Generally, information is either deduced from naming conventions or from surface normal vectors. For instance, the material name assigned in the CAD program is matched to a construction assembly from Sustain’s assembly database. This allows Sustain to differentiate windows and doors from their parent surfaces. However, it is not sufficient to differentiate walls, floors, and ceilings since certain assemblies might be used for all three interchangeably. The surface normal vector makes this differentiation possible. User specified adiabatic and below-grade surfaces are also detected at this point, as well as forced thermal zoning through CAD layer names. The third step involves heuristics that examine surfaces in the context of their surroundings. For instance, a surface may be defined as a ceiling given its normal vector, but may be upgraded to a roof with knowledge of its relation to the building as a whole. At this level, the exterior envelope of the building is defined and windows and doors are associated with their parent wall surfaces. The fourth and final automated step is the caching of other data relevant to simulation. This data, such as surface areas, space volumes, and overall floor and envelope areas, is sufficiently simple enough to calculate that it does not need to be saved with the model, but still only needs to be recalculated once when the model is opened by the user. 4.2. Zone finding One of the more difficult tasks when transforming or creating digital models for building energy simulation is the definition of the “zone”. Architects and engineers use the term “zone” to refer to programmatic zones, actual rooms, areas of control for

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Modeling Environment

Geometry Correction

Name and Orientation-Based Heuristics

Material Data

Context and Proximity-Based Heuristics

Simulation Input Caching

Thermal Zones

Building Data

Interface Process

Database External Software Data Flow Fig. 5. Geometric building data translation into thermal data. Arrow widths represent the amount of data passed between successive operations.

a mechanical system, and thermally similar building areas, usually interchangeably. With the trend towards greater complexity in building geometries and a consequent increase in the number of building zones, each with more local control, this problem is exacerbated. The definition of “zone” in BES is more straightforward – a zone is a collection of surfaces that define a space or spaces within which the air node is assumed to have consistent thermodynamic properties and values, and which can be controlled as a single air volume by simulated mechanical systems. We are experimenting with automated methods to extract simulation appropriate zone definitions within complex geometries from existing building models. Our original approach was to generate seed points and, in a sense, “paint with light” [29]. This approach creates hierarchical relationships which group surfaces into bins that correspond to actual physical spaces. Since thermal control zones in buildings usually encompass multiple discrete spaces, we are also experimenting with methods to automatically group spaces into thermodynamically rational zones. This has the added advantage of potentially reducing simulation times, since the execution time of the heat balance portion of the simulation calculation is directly related to the number of thermal zones in the model. Our approach, using resistorcapacitor network analysis [15] is explained in greater detail below.

6. Sustain graphical user interface

5. Building energy simulation (BES) module

6.2. Results visualization

As part of our research, we are implementing several simulation and pre-simulation modules: novel routines which compute direct solar radiation, diffuse solar and sky radiation, direct and diffuse radiation reflected from the environment, cluster driven parametric analysis, and RC network model decomposition. Descriptions of some of these new modules have already been published and are briefly described in Section 7. Sustain can also run external programs, such as EnergyPlus, Radiance, or global illumination renderers. Each external simulation program depends on the implementation of two functions. The first exports Sustain’s building data to the native format of the simulator. The second is executed after the external simulation and checks for errors, collects the output, and converts it to Sustain’s data format. Since the building data and the output data are hosted on network storage, all simulators can be executed either on the user’s computer or on a cluster node.

One of the major problems with energy simulations is that they produce enormous amounts of raw data which are difficult to understand. A single simulation run can generate gigabytes of information consisting of many interrelated time-dependent variables with trends and interrelationships that are hard to recognize. Parsing this multi-dimensional output and displaying the relevant information is a “data visualization” problem closely associated with data mining and graphical user interfaces. Using multiple five-dimensional output (3 geometric dimensions, time, and color) graphs, all visually related to the geometry of the simulated building, we have found a remarkable ability to correlate complex energy behavior. Designers have inherently strong spatial conceptualizations and are able to relate spatiotemporal information directly from the visual representations (Fig. 1). Using graphical output displayed simultaneously and visually related to a building’s geometric and thermal properties, it becomes

As shown in Fig. 2, we have developed a novel graphical user interface to simplify the CAD model input tasks and to visualize the simulation results. By always displaying the building geometry, the user can interpret energy simulation results and relate output data to the relevant portions of the input model. With our three-screen display (Fig. 1), we use the left screen for input, the center screen for model visualization and control, and the right screen for displaying simulation results. 6.1. Model visualization and control The BEM model is always displayed in the center panel with its proper orientation, the sun path and shadows calculated from the sun position at any point in time. The user can interactively view the building and terrain from any position using the graphical navigation controls. Weather data for any time period can be displayed to help correlate the heat loss/heat gain for any surface or portion of the building. It is important to show this information so that the user can relate the output data to the input through the model geometry, orientation, position of the sun, and time of year. Although not yet fully implemented, the dashboard is used to summarize simulation results for any time increment and compare predicted behavior with code standards or other design options. In this way, design flaws can be revealed and different design strategies can be evaluated.

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easier to understand a particular building’s thermal dynamics. But how can this be used to design? For example, simulation results may indicate that cooling loads exceed a desired maximum, but they do not suggest how changing certain parameters might affect the system as a whole, making it difficult to determine which strategies to adopt. Preferably, any system of thermodynamic analysis that will be used in a design process should relate simulation results to a recognized benchmark, and ideally should display and compare similar simulations in which basic parameters (form, orientation, envelope thermal mass values, etc.) are varied parametrically. This relational information will allow designers to “close the loop” in an iterative goal oriented design process. (See Section 7.7) 7. Current research investigations As stated previously, the goals of Sustain were to create a framework to support three primary user groups: researchers, designers, and teachers. During the first few years most of our effort has concentrated on serving the research community. Specifically, we have focused on developing routines to simplify the input tasks for creating BEM models and on creating novel methods to accelerate the simulation calculations. We have tested and evaluated energy simulation routines at the module and function levels and assessed the benefits which might accrue if the methods used could be refined using modern computer graphics algorithms. All of our research has used EnergyPlus as a primary thermal simulation engine, as it has publicly available source code. In our initial profiling studies of EnergyPlus, we found that a dominant portion of the total computation time occurs in a relatively small number of modules. A large portion of our initial research involved determining the amount of radiation reaching the external surfaces of a building. This includes direct solar radiation, diffuse sky dome radiation, and the secondary radiation effects from the surrounding environment, including neighboring buildings, terrain, and foliage. These phenomena depend on computationally expensive shadow calculations, an accurate sky model, as well as the visibility analyses between building components and the surrounding environment. Similar characteristics exist in the calculations of the radiant energy exchange between internal building surfaces. We include descriptions of two other relevant investigations. The first is a strategy for automatic model simplification which will reduce simulation execution times. The second describes our initial work on analyzing and visualizing the results of parametric studies to aid the design process. We expect that during the course of our current research project we will discover additional modules and new interface technologies that will further accelerate the computation and streamline workflow. The following descriptions indicate some of the modules we have developed or are developing. 7.1. Shade and shadow calculations Accurate calculation of direct solar radiation is one of the most important but computationally expensive routines necessary to predict a building’s thermal performance. The problem is exacerbated by the fact that shading devices, brise-soleils, overhangs and other self-shadowing geometries are commonly used design strategies to reduce direct solar gain. Due to the sun’s continuously changing position, the potential complexity of a building’s geometry and the occlusion effects of the surrounding environment, current methods used by validated simulation software take too long to compute. Yet regulatory energy guidelines such as ASHRAE 90.1 Appendix G require a building to be simulated through a full year five times [30]. To reduce computation time, standard practice is to use large time steps in the calculation of radiant forcing

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functions (e.g., every 20 days) and simplified geometries which reveal trends in building energy consumption but is not very precise. We should not be limited by these constraints. Computing the effect of the shading devices mentioned above is usually done in one of two ways. The first method, used by EnergyPlus, analytically computes the shadow area on a receiving surface by identifying the elements creating the shadow [31,32]. This analytical approach to shadow calculation currently is slow, generally limited to simple geometries, and susceptible to error in typical architectural models with large numbers of surfaces [33]. A second approach is to use a parallel ray-casting algorithm which requires many samples for accuracy and is also computationally very inefficient. We use computer graphics hardware occlusion algorithms, or pixel counting procedures, to accurately calculate the sunlit portion of each external building surface. Recognizing that the sun’s motion and the resulting shadow areas change in a continuous fashion, we compute parametric spline representations as a three-dimensional function for each building surface with daily and yearly time as the parametric variables. For a given building geometry and orientation, this abstraction reduces computational complexity by allowing a continuous interpolation method. Using these b-spline surface techniques combined with occlusion algorithms [34] developed in computer graphics, our mathematical and hardware implementations are extremely fast. We do all this in a pre-processing step. The precise details of the algorithm are presented in [35]. We have tested this algorithm on several building models, each with intricate shading devices and seen a significant reduction in computing time. For example, in Fig. 6 we gradually increased the complexity of the geometrical model with only a minimal increase in pre-processing time and no effect on the energy simulation time. We also have been able to show that even complex shading devices such as those used for the New York Times Building (>1 million polygons) can be modeled and analyzed in a reasonable amount of time [36]. More recently, we have built on the previous work by utilizing programmable shaders and graphics hardware acceleration for the computation of direct solar radiation through transparent (translucent) shades and perforated screens. Architects frequently use these devices since they can significantly limit the effect of direct solar radiation but still preserve views. Such shading devices have been used on numerous buildings, but determining the impact and efficiency of these shading devices again has been too computationally expensive [37]. 7.2. Environment mapping 7.2.1. Direct sky To calculate the radiant energy emanating from the sky dome and impinging on a building’s external surfaces we use a different procedure. In this case it is necessary to compute the energetic contribution from each segment of the visible sky. We use an early computer graphics algorithm, known as “environment mapping,” which is used to approximate the visible effects of global reflections [19]. By assuming that all surrounding objects and light sources in the environment are sufficiently distant, these effects can be rendered as a two-dimensional texture determined by ray casting. The analogy is that an object is positioned in the center of a hemisphere of infinite radius, and the resulting texture, indexed by polar coordinate angles is “painted” on the object’s surface. For energy calculations, we use this same analogy and place the hemisphere on each polygon of a building’s external surface. For example, the hemisphere shown in Fig. 7a depicts the visible sky dome from a horizontal roof surface of a building placed

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Fig. 6. Blaas general partnership headquarters with simplified and complex models.

at that location. The blue region on the hemisphere represents the visible sky, and the black indicates the occluded portions. Our hemisphere is divided into uniform solid angle segments for sampling purposes and stored as a texture map (Fig. 7b). Note that no energy contributions need be computed for segments in the occluded regions. Further details on this algorithm are presented in [38].

the appropriate order of computations to accelerate the simulations. We need only sample the occluded (reflecting) surfaces (black) from Fig. 7b, and build up several texture maps (Fig. 7c, d and e) simultaneously. Despite the immense computational demand, the algorithm has proven to be fast, accurate, robust and scalable.

7.4. Solar and sky domes 7.3. Indirect (environment) To simulate the indirect reflections from the environment we use the techniques described in the previous section. For each hemispherical segment, we cast rays to determine the first surfaces struck and the possible energy transport paths. Our assumption is that radiant energy reflections from the sun and sky can be subdivided into diffuse and specular components [39]. Several potential paths, from both the built environment and the terrain are shown in Fig. 8. We accumulate the effect from both the diffuse and specular transport chains for both the sun and sky dome sources, and again store the resulting data as texture maps (Fig. 7c, d and e). This computation must be repeated for each exterior building surface and for every sample direction. At present, we limit the effects to the first bounce indirect specular reflections from the sun and the first bounce indirect diffuse reflections from both the sun and sky dome, although the procedures are extensible to multiple bounces. Sampling the environment requires determining millions of ray-surface intersections for all relevant transport paths in complex polygonal models. Since these calculations must be performed for each exterior building surface, it is important to establish

The diffuse component of solar radiation is generally recognized as a significant fraction of the total incident solar energy available to a solar receiver. How to accurately and efficiently evaluate this diffuse radiation is a crucial problem for solar energy estimation and motivates the research for an accurate sky model. For radiant energy computations, it is important to have a physically accurate wavelength dependent scattering model of the sky dome. Such a model would benefit building energy simulation by allowing the anisotropic distributed sky radiance from arbitrary directions in the sky dome to be easily and accurately determined. The wavelength dependency will also allow daylighting and energy simulations to incorporate the wavelength-dependent coatings of future windows and shading products. Lastly, the same scattering model can be used to more accurately simulate the infrared energy heat gain and heat loss from buildings. Sky models have been investigated in both computer graphics and energy simulation. For energy simulation, the state-of-theart method is the CIE sky model, which is widely applied to compute the sky luminance [26]. The CIE model derives parameterized analytical formulae to compute sky luminance and fits the parameters in the formulae for different sky conditions based on scan measured sky luminance datasets. The CIE model is a valid

Fig. 7. Complex environment geometry is projected onto a hemisphere, and data are stored as two-dimensional texture maps.

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Fig. 8. Direct and indirect energy transport paths from the sun (a) and sky (b). ED and ES represent the diffuse and specular reflection transport paths.

statistical approach, but it can only generate approximate sky luminance estimations from the whole sky dome, rather than accurate directional incident sky luminance at specific locations and times. The model provides fifteen parameter sets to predefine fifteen different sky conditions, such as clear, partly cloudy, and overcast. Hence, a specific sky condition can only be generated by the interpolation between different predefined cases. Such interpolation inherently introduces errors for sky conditions when the luminance distribution is anisotropic. In the computer graphics field, Nishita [40] proposed a sky rendering method to physically simulate the absorption and scattering behavior of the skylight in the atmosphere and achieved a photorealistic rendering of the sky appearance. Graphics researchers [41] also developed a skylight model that approximates the full spectrum of daylight by combining the luminance computed by a sky model introduced by [42] and the chromaticity derived by physical light simulation. Recently, with the evolution of computer graphics hardware, several methods have been proposed to efficiently render sky appearance based on GPU-based pre-computation [43]. For most graphics applications the visual fidelity of the sky model is the only important priority. These methods do not consider the physically-based atmospheric properties at specific locations and thus it is inappropriate to directly apply the graphics models for building energy simulation. To overcome these limitations, we have developed a new fullspectrum sky model which is based on the physical simulation of solar energy transportation across the local atmosphere (Fig. 9a). To ensure the physical accuracy of the simulation, we firstly build up a layer-based local atmosphere model. As shown in Fig. 9b, the layer position follows the exponential atmospheric density distribution based on altitude from the ozone layer to the ground. Within each layer, we assume that the optical properties of the atmosphere, such as the scattering and absorption coefficients of aerosols and the scattering coefficients of air molecules, are uniform. For a specific geographic location, we compute these optical properties by either refining the output of the Community Earth System Model (CESM) simulation or using OPAC software [44]. With the established local atmosphere model, we mimic the precomputation-based sky rendering strategy [45,46] and simulate the sky over the relevant full-spectrum. Based on our experimental tests, the pre-computation for a specific atmosphere condition requires just several seconds and the energy transport simulation for each surface receiver point can be completed in approximately ten milliseconds. Although the local cloud condition is a major factor in the scattering behavior, presently we only consider the clear sky case.

7.5. Intra-zonal radiant energy exchange Radiant heat transfer accounts for the thermal radiation emitted and absorbed between surfaces, and is a major component of the flow of thermal energy within an environment. In the thermal regime, every surface emits radiation and is thus a source. This radiation can travel long distances and may reflect off other surfaces before being absorbed at a receiving surface. Thus the radiant interchange couples all the surfaces within a region or zone, making it potentially expensive to solve. While there is considerable energy in the thermal radiation, the net energy flow is much smaller. This makes finite element methods, commonly referred to as radiosity methods in the computer graphics literature, a good choice for simulating radiant energy transfer [47–49]. The environment is first subdivided into surfaces which are approximated as having piecewise constant temperature. The radiant coupling between surfaces is encoded into a transport matrix that includes the view (form) factors between surface pairs and their reflectivities. Once the emission for each surface is known (based on temperature), we can then solve a matrix equation to get the net radiation flow for each surface. In architectural environments, the full matrix radiosity method described above is a very efficient solution. A zone can typically be adequately modeled by a small number of surfaces (typically less than a hundred), while the radiant transfer needs to be solved at a very large number of time instants (over hours, days, or years). Thus for intrazonal radiant interchange, we can execute a pre-process that constructs and inverts the transport matrix and rapidly solve the radiant heat flow at each time step. To compute the transport matrix, we first compute the formfactors between each pair of surfaces (Fig. 10a). Although analytic form-factor solutions exist for special cases [50], in general they must be estimated numerically [51]. We use ray-casting which robustly handles the general case and allows the choice of surfaces to be independent of the underlying geometric representation. That is, a single surface can contain arbitrary geometry (multiple triangles, polygons, etc.). The matrix inversion can be accomplished by standard optimized libraries and cached for later use at each time instant. For a prototypical zone consisting of 1000 polygons, we build and invert the transport matrix in seconds and solve for the heat flow in milliseconds per time step. 7.6. Hierarchical model reduction As a BEM and BES platform, the objective of Sustain is to enable the throughput of increasingly complex models as part of a

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Fig. 9. Scattering sky model (a) with atmospheric layers (b).

performative design process. Unfortunately, model complexity gives rise to increasingly unacceptable simulation times and incomprehensible volumes of data. Using our graphical user interface, we are experimenting with a new technique that partially addresses this challenge by aggregating thermal aspects of the model. By reducing the model to its dominant elements, the method addresses all three bottlenecks in building performance simulation. First, it replaces skilled, labor-intensive manual model reduction with an automated, transparent, and tunable process. Second, by reducing BEM complexity, it reduces simulation time. Third, it reveals insights into thermal coupling in a direct and intuitive visual format, allowing the designer to understand nontrivial aspects of the building’s thermal performance that would otherwise not be evident from raw simulation data. To perform this hierarchical model reduction, it is first necessary to convert the object-oriented building model into a form amenable to numerical analysis. The use of a model abstraction, such as a resistor-capacitor (RC) network, is particularly well suited to spectral decomposition methods [52,53]. The abstraction layer formulates the RC network using the layer composition of each wall, the environmental setting of the building, and a material property database. Connecting the wall circuits to interior air capacitors and to special outdoor air and ground capacitors completes the circuit abstraction (Fig. 11a). The zone air capacitors map directly to zone

objects in the BEM, and the terminal wall capacitors map to surfaces, thus allowing model aggregation results to be mapped back to objects in the BEM. The entire translation process is transparent, and thus manual work is completely avoided. To be readily applied and interpreted, model reduction results should be representable by a progression of changes that can be applied directly to the BEM. Thus, each model simplification step should not undo changes made by previous steps. Our hierarchical model aggregation uses a tree structure that encapsulates such a sequence (Fig. 11b). First, the abstracted model is grouped into one large cluster. Next, the cluster is split into two pieces along its weakest internal thermal connections. This process repeats recursively until no more clusters can be divided. When more than one cluster is available for division, the one yielding the best improvement in simulation accuracy (as given by a Kullback-Leibler metric [53]) is chosen for that step. The resulting tree contains only one division per level, so bottom-up traversal progressively combines capacitances in the RC network to reduce its complexity. Translation of this tree from the RC abstraction back to the BEM completes the process. As the BEM tree is traversed and simplifications are applied to surfaces and zones, simulations take less time but become correspondingly coarser. Because the model simplifications are applied to the BEM, the improvements in simulation speed are independent of the underlying engine.

Fig. 10. Radiosity form factors (a) and example computer graphics radiosity solution (b) [49].

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Fig. 11. Model reduction using RC networks.

The thermal implications of complex models are difficult to glean from raw simulation data but are readily apparent when presented in terms of the building geometry. A graphical interface allows the designer to traverse the aggregation sequence step-bystep to view not only thermal coupling among progressively larger portions of the building, but also the relative significance of each thermal relationship. As shown in Fig. 11c, the exterior walls of the foreground rooms are strongly coupled to each other and could be modeled as a single wall, and the rooms themselves can be merged into a single room; moving the slider one increment to the left reveals that they are more strongly coupled to the adjacent room than to the hallway. In complex designs, subtle thermal coupling due to thin or highly conductive partitions between zones can be similarly revealed. This insight is especially valuable when thermal coupling is not evident from geometry alone. Thus, with a suitable automated abstraction, a numerical model reduction method can be applied to the object-oriented BEM, providing the designer with new insight into a building’s energy performance (BES) and delivering faster simulation times without added effort. The method is more fully described in [15]. 7.7. Parametric analysis It has long been understood that running a single energy simulation of a proposed design is of minimal value to the design process itself. The fundamental problem is that a single simulation may describe energy use but not how to change it. This information can only be developed through the careful testing of design alternatives. Unfortunately, given the time requirements, expense, and difficulty of running whole building energy models, the use of

iterative simulations to test multiple alternatives is necessarily limited in practice. Thus, we find that BES generally is not being used to develop actionable intelligence during the design process where it could have the greatest impact on future building energy use. Since the Sustain platform offers improvements in ease of input, model malleability, and reduced simulation run-times, iterative simulations are possible even during the earliest stages of design. However, iterative testing of alternatives in and of itself does not give an accurate picture of the large domain of a multi-variate design space. Understanding this design space is critical to making design decisions. Until recently, understanding and simulating a design space delimited by more than a few variables using BES has been considered impractical. Attempts have been made to navigate such spaces using optimization algorithms and other search techniques but such methods tend to be capable of finding localized optima and fail to give an accurate picture of either the design space topology or the nature of variable interdependencies. The Sustain platform, however, can provide a framework for studying such large parametric design spaces. Using Sustain’s abilities to interface with lightweight custom plug-ins for CAD programs, to initiate and control multiple simultaneous simulations, and to visualize the results, we have built a system that can evaluate complete design spaces [54]. In these parametric studies, the primary difference is that instead of building a single model in CAD software, we begin by defining a parametric model using either SketchUp or Grasshopper and Rhinoceros and a suite of custom plug-ins. The parametric model is then enumerated and translated producing an n dimensional array (where n represents the number of discrete vari-

Fig. 12. Parametric model with voxel plot output and batch controller (inset).

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ables) of BES models. The models are simulated in parallel on our compute cluster using EnergyPlus through a process initiated and controlled by the system manager and control node. Typically, we request a limited number of macro-scale dependent variable outputs from the simulator, because the potential amounts of output data that can be produced by such parametric runs is very large. Such dependent variables as typical yearly energy consumption or CO2 e emissions normalized by unit area allow for statistical analysis of the design space. The results from the parameterized simulation runs are simultaneously displayed in three windows and updated interactively. Sustain shows a detailed view of the building alongside a three-dimensional voxel-plot of the simulation results and a multi-axis batch controller for data navigation (Fig. 12). With this graphical feedback, one can easily find regions of the design space that both perform well and lie in a discrete zone of a particular axis of variation. One of the most interesting results of our work to date has been to show that in certain design spaces the regions of high performance are non-contiguous. Practically this means that variable combinations that produce very different buildings may perform equally well due to the nature of variable interactions. We have also carried out experiments to determine the utility of statistical analysis and to visualize the relative impacts of parameter variation. Of particular significance is the ability to inform the designer which axis of variation has the greatest impact on the dependent variable at a particular point in the design space. This is useful because, when designing high performance buildings it is critical to determine which characteristics of a particular design have a large impact on overall performance, and which characteristics do not and thus are relatively fungible and can be tuned to meet criteria that cannot be explicitly enumerated in the parametric model [55].

8. Conclusion Over the past several decades, modern computational environments have been improving at exponential rates. Unfortunately, the power of this modern computation and simulation technology has not been adequately applied to the fields of architecture and sustainable design. Yet an essential component for the design of a more sustainable built environment would be to utilize this rapidly improving technology. This paper described a novel test bed environment which offers an easy-to-use interactive graphical interface, provides access to innovative simulation modules that run at accelerated computational speeds, and presents new graphics visualization methods to interpret simulation results. The system is significantly easier to use compared to currently available building energy simulation tools. Most of our improvements in the accuracy and execution speed of the simulation modules are based on the modification of advanced computer graphics rendering algorithms. The performance improvements are illustrated with several real examples implying that performance-based as contrasted to prescription-based metrics may be increasingly used in the future. We hope that our initial test bed, Sustain, or ones similar in concept, will advance the state of the art in the domain of whole building energy analysis and building performance simulation. Specifically, we had several goals which are enumerated below. Our first goal, for the design profession, was to demonstrate that the concepts of our prototype system have the potential to significantly impact the process of designing and engineering the built environment. By making thermodynamic analysis available early in the design process it enables architects to analyze the impacts

of form, siting and material choices on energy consumption at a very early stage of the design process. This enables clients to make informed choices between alternative schemes at the time when such choices have the greatest impact. It allows architects to collaborate more effectively with consulting engineers, as they are able to identify particular synergies between the performance of the building envelope and HVAC and lighting systems. Being able to visualize simulation results in relation to the geometry of the building reduces the possibility of model input error. This in turn improves both the efficiency and cost effectiveness of the total design process. Furthermore, these advances in data visualization enable better communication both between members of project teams and within the discipline itself, leading to more consistent and comprehensive analyses and better communication of relevant information. As simulation tools become more accessible to and accepted by the profession, the transition from prescriptive to performative energy codes based on energy intensity or carbon emissions per unit area becomes easier. This transition is particularly desirable for the architectural and engineering professions as the solution set that encompasses buildings that meet prescriptive code requirements is inevitably smaller than the set that meets performative requirements. Consequently, the full adoption of performative codes will allow architects and engineers greater freedom and will provide the opportunity to develop innovative solutions for the design of buildings that meet the constraints in terms of energy use and carbon footprint. Our second goal was to create a research platform that can address the needs of the building science community for fast, easy to use thermodynamic analysis. Any particular research endeavor that depends on such analyses must devote significant resources to establish this type of computational base, despite the fact that the research topic itself may have a different focus of inquiry. The test bed described within this paper achieves this goal with an easyto-use interactive graphical user interface, innovative simulation modules that run at accelerated computational speeds, and new graphics visualization methods to interpret simulation results. We hope that the availability of interactive graphics systems like Sustain will stimulate the creation of platforms for future research in energy simulation technologies. Third, the prototype system described has the potential to influence the education of both architects and engineers. The processes of design learned early during one’s professional training have a significant impact on how people work throughout their careers. The current state of simulation software precludes its inclusion as a fundamental component of a pedagogical structure, especially in architecture. Thus the existing basic learned design process does not holistically incorporate performative testing and iteration, a necessary prerequisite for the next generation of designers to create future sustainable buildings. We also believe that the concepts of this work could help by reducing the level of expertise necessary to perform such tests, allowing students to produce energy analyses as easily as they now create computer renderings of design proposals. The proposed work could also create a common platform that can be used in interdisciplinary teaching as it allows for both quantitative and qualitative analyses of built form.

Author’s note The software system described is in a constant state of flux, and although operational, is continuously being modified. Many of the specific algorithms described in Section 7 have now been published, primarily in the proceedings of the International Building Performance Simulation Association conferences of 2011 and 2012. When

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the modules have been tested and verified, we send the computer code to the Department of Energy with the hope that it will either be incorporated into future versions of EnergyPlus or made available under the Apache license. To date, only the direct solar radiation module (Section 7.1) has reached this stage. Acknowledgements This material is based upon work supported by the Department of Energy under Award Number DE-EE0003921. Initial development of Sustain benefitted from the intellectual contributions of Professor Ken Torrance and was supported by Cornell University’s David R. Atkinson Center for a Sustainable Future (ACSF) through the Center’s Academic Venture Fund. In addition to the authors, many students contributed code and test models to the project, including Edgar Velázquez-Armendáriz, Raymond Fort, Savina Kalkandzhieva, James Sherman, Colin McCrone, Andrew Heumann, and Nicolas Savva. Disclaimer: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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