ARTICLE

Large-scale building simulation using cloud computing forestimating lifecycle energy consumptionRussell Richman, Hayes Zirnhelt, and Stuart Fix

Abstract: The use of whole building simulation is increasing to support the design process. Often it is desirable to evaluate manyscenarios, however the simulation time involved presents a significant barrier. Simulationists are forced to reduce the numberof scenarios evaluated to meet time constraints. With cloud computing, simulationists can significantly reduce the totalsimulation time by allocating portions of the simulations to multiple processor cores. The benefit of cloud computing isdemonstrated through a case study project, which computes the lifecycle energy consumption (LEC) of 1 080 000 single detachedhome design scenarios in Toronto, for a budget of CAN$2400. Code was written using Python to couple EnergyPlus and ATHENAIE to modify input files, process results and calculate LEC. The results of this study suggest that utilizing cloud computing tosimulate large scenario studies represents an efficient method that is beginning to surface in mainstream building simulation.

Key words: automated, mass simulation, cloud computing, pilot study.

Résumé : La simulation intégrée des bâtiments prend de l’importance dans le soutien du processus de conception. Elle estsouvent utile pour évaluer plusieurs scénarios. Le temps de simulation représente toutefois un obstacle important. Les profes-sionnels de la simulation sont forcés de réduire le nombre de scénarios examinés afin de rencontrer les contraintes de temps. Enutilisant l’infonuagique, les professionnels de la simulation peuvent réduire considérablement le temps total de simulation endistribuant des portions des simulations a des processeurs a cœurs multiples. L’avantage de l’infonuagique est démontré par uneétude de cas qui calcule la consommation d’énergie pendant le temps de cycle de vie (LEC) de 1 080 000 scénarios de conceptionde maison individuelles a Toronto, pour un budget de 2400 $C. Le code a été écrit en utilisant Python pour le jumeler aEnergyPlus et ATHENA IE afin de modifier les fichiers d’entrée, traiter des résultats et calculer la LEC. Les résultats de la présenteétude suggèrent que l’infonuagique dans la simulation de grandes études de scénarios représente une méthode efficace quicommence a être utilisé dans la simulation ordinaire de bâtiments. [Traduit par la Rédaction]

Mots-clés : automatisé, simulation de masse, infonuagique, étude pilote.

1. IntroductionA successfully designed low-energy building requires the opti-

mization of both the impact associated with each individualbuilding and site design element and the impact associated withthe interaction of all design elements together. The use of build-ing simulation tools plays a critical role in the design process ofsuch a building. This paper highlights the benefits of cloud com-puting through a preliminary study that couples lifecycle analysis(LCA) software with a whole building energy simulation programto compute lifecycle energy consumption. The study shows howthe use of cloud computing can execute large-scale simulationprojects that are computationally demanding. Simulation runtime has been the topic of several recent studies (e.g., Hong et al.2008; Garg et al. 2011) and has been identified as a barrier tofurther integration of simulation into the design process (Bleil deSouza 2009). By reducing simulation run time, cloud computingcan specifically benefit: (i) studies with large simulation sets, (ii) theverification of optimization techniques, (iii) reducing overall sim-ulation time for large-scale optimization projects, and (iv) provid-ing quick feedback in the various stages of building design.

The study quantifies a reduction in simulation time with fur-ther discussion on the benefits for future large-scale simulationprojects. EnergyPlus was chosen to complete whole building sim-ulation in addition to Athena’s Impact Estimator for the embod-

ied energy calculations due to its relevance to North America. Thispaper contributes to the specific problem of simulation run time.A preliminary study comprising over 1 000 000 separate buildingsimulation iterations is used as a back-drop to present the benefitsof cloud computing, specifically addressing organization and re-ducing simulation run time.

The paper begins with an outline focusing on mass buildingsimulation coupled with uses of optimization techniques and theperceived benefits of utilizing cloud computing. A preliminarystudy is introduced with the methodology described to highlightthe use of cloud computing. Specific challenges required to beaddressed for this approach to move further within industry andacademia are presented. Preliminary results from the study areproduced to highlight the nature of generated data and associatedtrend analysis.

2. Background

2.1. Cloud computing technologies used to aid buildingdesign

Foster et al. (2008) place the origins of cloud computing in thedevelopment of computing grids in the early 1990s; the basic con-cept being access to large-scale computing power on demand.Several large-scale federated grid systems existed, however nocommercial grid computing services existed in the early 1990s

Received 10 May 2013. Accepted 17 January 2014.

R. Richman, H. Zirnhelt, and S. Fix. Department of Architectural Science, Faculty of Engineering Architecture and Science, Ryerson University,350 Victoria Street, Toronto, ON M5B 2K3, Canada.Corresponding author: Russell Richman (e-mail: richman@ryerson.ca).

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Can. J. Civ. Eng. 41: 252–262 (2014) dx.doi.org/10.1139/cjce-2013-0235 Published at www.nrcresearchpress.com/cjce on 22 January 2014.

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(Foster et al. 2008). Since then, large computing corporations real-ized the benefit to provide large-scale computing power throughvirtualization. Hence, cloud computing has the ability to completeextremely large-scale computing projects with relative ease andextremely flexible access. Further information on the history anddefinition of cloud computing can be found in Foster et al. (2008)and Wang et al. (2011).Various cloud computing technologies areutilized to complete large-scale simulation based research to aidbuilding design. Li et al. (2011) evaluated various high perfor-mance computing technologies to parametrically model the ef-fect of varying meteorological conditions on building thermalperformance. The work provides a good overview of cloud com-puting technologies and recommends further research throughcase study evaluations. Castane et al. (2013) argue that cloud com-puting is a valid method to conduct parametric building energyanalysis and suggest the framework for conducting such researchrequires formalization to streamline both output and simulationtime. Several current cloud computing based approaches are pre-sented. Naboni et al. (2013) used the VENUS-C online cloud servicein conjunction with jEPlus (a parametric shell for Energy Plus) toconduct parametric simulation on approximately 220 000 build-ing design options. This work showed an approximate reductionin simulation run time of over 95% and the ability to inform thereduction in building energy use to a higher degree than theconventional design process. Burton and Shaxted (2012) utilizedprivate and cloud computing clusters along with jEPlus to runover 300 000 designs to investigate the interrelationship of mul-tiple energy conservation measures as an aid for building design.In comparing various methods to conduct large-scale simulation,this research estimated an approximate 93% reduction in runtime for the specific simulation set. The research in this paperutilized a similar methodology to Burton and Shaxted (2012) andNaboni et al. (2013) in so as EnergyPlus was utilized to conductparametric analysis of multiple building design options utilizingprivate cloud computing. The research advanced previous litera-ture by: (i) increasing the amount of simulations to over 1 000 000and (ii) incorporating an additional aspect of lifecycle energy intoparametric analysis.

It appears cloud computing is in its early stages as applied toparametric building energy simulation, although numerous stud-ies exist for its use in other fields such as computer engineering.The research in this paper serves to contribute to pioneering theuse of cloud computing to aid building design and addresses thespecific benefit of reducing simulation time with a relatively largedata set compared to the literature.

2.2. Benefits of large-scale simulation using cloudcomputing

Much of the published literature on cloud computing highlightthe benefits associated with reducing simulation run time.

1. Many studies require a large number of simulations

Kneifel (2010) carried out 576 simulations for a study quan-tifying lifecycle carbon emissions and lifecycle cost of variousenergy savings measures for office buildings. Hasan et al.(2008) used the U.S. Department of Energy’s (DOE) GenOptOptimization program and the IDA Indoor Climate and En-ergy (IDA ICE) dynamic simulation program to minimize thelifecycle cost of a typical detached home in Finland. Theycompared the results to a brute force search applied afterreducing the number of scenarios analyzed by using heuris-tics from over four million, down to 6400. The brute forcesearch required 73 h of simulation time for 6400 scenarios.

Also, some parametric studies require a large number ofsimulations and cannot be accomplished using optimizationalgorithms. Key trends between the start and finish of the

study may be lost by the nature of the algorithm’s foundationof ‘survival of the fittest’. One example is a study by PPGIndustries Inc. (2007). They carried out 288 simulations usingDOE2, to provide relevant energy savings estimates for the useof high performance glazing in commercial buildings. Thelarge number of simulations were required to generate resultsthat were specific to 12 locations in North America, two build-ing types, and a range of building options.

2. Cloud computing can be used to verify optimization tech-niques

Metaheuristic type optimization algorithms are often usedin building optimization as they are able to avoid the localminima prevalent in the nonlinear, non-smooth solutionspace generated by building simulation programs during anoptimization procedure (Kämpf et al. 2010). Convergence of-ten cannot be proven for these functions (Kämpf et al. 2010),thus large-scale simulations may be useful to confirm theseresults. This is especially true as new metaheuristic algo-rithms are developed. As previously mentioned, Hasan et al.(2008) used a large-scale simulation to perform a brute forcesearch to confirm the results of the GenOpt optimization pro-gram. Magnier and Haghighat (2010) suggest that more researchis required to confirm the accuracy of the use of artificial neuralnetwork (ANN) in building optimization problems. Further,Kämpf et al. (2010) state that experiments are often required todetermine which set of optimization algorithms are effectiveon a given type of problem. Brute force searches offer theability to arrive at the true global optimum within the boundsof the uncertainty in the input parameters. For many studies,exhaustive searches are not practical due to the very longsimulation times required; sometimes it is not even possible.Thus, cloud computing provides an option to conduct exhaus-tive searches (brute force technique) within a reasonabletimeframe.

3. Large optimization projects may require long simulationtimes and thus could benefit from using cloud computing

Hamdy et al. (2010) used a modified genetic algorithm (GA)to optimize a house for minimum operational greenhouse gas(GHG) emissions. Their study was limited to eight design pa-rameters and required 44 h of simulation time and 1010 sim-ulation iterations to find the optimal results. Using cloudcomputing this could be accomplished much faster, and moredesign parameters could be evaluated. For example, this sim-ulation time could be reduced to approximately one hour, byrenting 44 virtual instances from a cloud network. Magnierand Haghighat (2010) carried out a multi-objective optimiza-tion study for a single family dwelling. They used a trainedANN and a GA for optimization of energy consumption andthermal comfort with 20 design variables. The total simula-tion time was three weeks. Without using the ANN they esti-mate the simulation time would have been 10 years. Usingcloud computing could reduce optimization times substan-tially.

4. Reducing simulation time — quick feedback is essential dur-ing the design process

It is generally accepted that the integrated design process isessential to achieving high performance buildings (Lewis 2004;Zimmerman 2011 ) and that building simulation plays a criticalrole (Hirsch et al. 2011). In the early and preliminary designstages, quick feedback is essential. To achieve this, simplifiedmodels are often used, however the enhanced speed mayequate to a loss in accuracy. Hirsch et al. (2011) found, for thedesign of a National Renewable Energy Laboratory (NREL) fa-

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cility, the energy modelling process was squeezed into aschedule that was primarily based on design documentationand construction, creating tension between the design time-line and the level of model detail required for accuracy. Al-though a considerable amount of time is required to set upthe simulations, simulation run time is also an importantfactor. Once detailed modelling is required, cloud computingcould be employed to provide rapid feedback to the designteam, this could then help to speed up the entire design pro-cess and would also allow for more options to be evaluated.Bleil de Souza (2009) points to the need for “instantaneous”feedback in the design process, as an important bridge be-tween the engineering and architectural design approachesand as an important step towards further integration of sim-ulation into the design process. Although instantaneous feed-back may not be a reality for some time, cloud computing canbe an important step in this direction as it can substantiallyreduce simulation time.

2.3. Energy simulationAlthough EnergyPlus is considerably more advanced than its

predecessors DOE-2 and BLAST (Crawley et al. 2004), its majorlimitation is longer simulation time (Garg et al. 2011). For exam-ple, for a hospital building simulated with 15 min timesteps, Honget al. (2008) found that EnergyPlus was 196 times slower thanDOE-2.1E. The slower run time is perhaps one of the reasons whymany building simulationists continue to use DOE-2. Recently,Garg et al. (2011) developed a simulation method for reducing therun time of single EnergyPlus simulations.

Parametric analyses often require a considerable amount of setup time. This can be reduced by using a parametric software pro-gram, however few are available for use with EnergyPlus (Zhang2009). GenOpt (Wetter 2009) can be used for parametric analysis,however its limitation is that it does not support non-numericvariables or arbitrary lists of alternative values (Zhang 2009).

2.4. Lifecycle analysisAs buildings become more efficient the embodied energy typi-

cally increases due to the additional insulation and more complextechnology required (Verbeeck and Hens 2007, Bowick et al. 2010).For typical buildings embodied energy can be in the range of 15 to20% (for review see Sharma et al. 2011), however for low energybuildings such as Passive Houses, and for Net Zero Energy Houses,embodied energy tends to be much higher (Verbeeck and Hens2007). Thus, to reduce energy consumption overall, the embodiedenergy of building materials should also be accounted for. Thetrade-offs between reduced operational energy and increased em-bodied energy are complex. For example, optimal insulation lev-els will depend on the materials chosen, the architectural design,and the type of heating system used. Therefore, many simulationsare required to find the optimal solution (or solutions) and iden-tify dominant trends.

3. Toronto pilot methodologyThis section provides a brief summary of the methodology em-

ployed in the Toronto pilot project and how cloud computing wasutilized to complete the work. The general methodology com-prised use of an open-source programming language to managethe parametric simulation of over 1 000 000 design packages uti-lizing a whole-building energy simulation software on a com-mercial cloud computing service. This output was coupled withembodied energy data and managed with an external databaseprogram. The methodology is suitable for use in other studieswith large-scale data sets as it represents a universally accessibleapproach with ability to be conducted on a small-scale budget andtimeline. This method is particularly transferrable to parametricbuilding studies as the whole building energy simulation software —

EnergyPlus — is highly rigorous and can accommodate both high-level and detailed alterations to building design. As such, themethodology is appropriate for addressing buildings at both thepre-design phase and system specific design phase.

3.1. Toronto pilot scopeThe Toronto pilot’s primary focus was on lifetime energy anal-

ysis. The optimal design was defined as that which causes minimallifecycle energy consumption (LEC). Lifecycle energy consumption wasa logical starting point as it required the primary results of energysimulation from which other energy-dependent parameters suchas GHG emission could be calculated without re-simulation. TheToronto pilot focused on a scope of 1 080 000 design scenarios,incorporating the range of parameters and parameter values de-scribed in the following sections. Five major tools were integral tothe Toronto pilot process: the Python programming language(V3), the EnergyPlus building energy simulation software (V4),Amazon’s Elastic Compute Cloud (EC2), the Athena Impact Esti-mator for Buildings (V4), and the FileMaker Pro database software(V11). The results of this preliminary study are not intended tostrongly inform design, but rather show a research success utiliz-ing cloud computing to conduct large simulation sets.

3.2. Python programming languageLarge-scale simulation involves the generation and manipula-

tion of massive amounts of data. To handle these demands, allwork for this research was written in the Python programminglanguage. Python is a dynamic object-oriented programming lan-guage that can be used for many types of software developmentand is distributed on an open source initiative (OSI) approvedopen source license that makes it free to use for all projects, on allplatforms (Python Software Foundation 2010). Python has strongmathematic, file manipulation, and web development modulesthat were especially helpful for this project. Python was used to:generate the 1 080 000 different EnergyPlus .IDF (or input data)files, parallelize and control twenty-four EnergyPlus simulationexecution streams, and manipulate the results data into the re-quired output format for a database. By using a coded structure,the optimization methodology is easily scalable for larger orsmaller projects and is highly repeatable.

3.3. EnergyPlusEnergyPlus is a building energy simulation program based on

DOE-2 and BLAST, with numerous added capabilities (for back-ground refer to EnergyPlus 2010). EnergyPlus was used to simulatethe operational energy performance of each design permutation.This software was selected for its text-based input capability, itsstrong simulation accuracy (Crawley et al. 2004), and its preva-lence within the field of building energy simulation research.

In the Toronto pilot, the following simulation parameters wereheld constant across all 1 080 000 simulations: building occu-pancy, internal lighting, internal gains, domestic hot water use,simulation zone density, ventilation rates, door performance,geographic location, insulation type, HVAC system, building ori-entation, exterior shading, exterior sheltering, and north windowto wall ratio. The manipulated parameters and values are shownin Table 1. Due to the preliminary nature of the results generatedfrom this study and its overall objective to support developmentof integrating cloud computing with building simulation, the con-stant and manipulated parameters were chosen based on a rubricincluding: computational power (i.e., the need to limit the totalnumber of permutations), anticipated effect on total life timeenergy, and computational manipulation (i.e., the ease at whichparameters could be modified using the existing automated ap-proach). The values chosen for the manipulated parameters typi-cally represent a range comprising extreme low and high values,specifically the building envelope performance parameters. These

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ranges were chosen while balancing the study of parametric effectand associated law of diminishing returns with currently avail-able extremely high performance elements used both in researchand advanced practice. A constant ratio between the thermal re-sistances of the slab, sub-grade walls, above-grade walls, and ex-posed ceiling was used to keep the scope within a reasonablenumber of simulations. This ratio is based on the ratio found inthe 2012 Ontario building code (MAH 2012).

3.4. Processing cloudLarge-scale simulation is now feasible for low-budget projects

because of recent developments in online cloud processing powerby Amazon Web Services (AWS). Working from a personal com-puter, one can create and instantaneously control virtual supercom-puter clusters, from any internet connection, that work dynamicallyto meet the user’s needs (Amazon Web Services 2010a). The Torontopilot required roughly 563 days of processing time to simulate theannual energy consumption of 1 080 000 home designs on a single2.5 GHz laptop processor based on initial tests by the authors. This1.5 year single core time was reduced to a single month by har-nessing 24 AWS virtual processing cores for a total rented cost ofapproximately CAN$2400. Each core could effectively be rentedfor the cost of CAN$0.085 per hour on a Linux platform (AmazonWeb Services 2010b). The total 1 080 000 simulations were ran-domly broken down into twenty-four 45 000 simulation subsets toparallelize the simulation process over the twenty-four virtualcores. Although the authors chose to use the AWS processingcloud, several other commercially available options exist andwould support this type of research.

3.5. Athena Impact Estimator for buildingsThe Athena Institute has developed the Impact Estimator for

buildings which is the only tool in North America for the lifecycleassessment of whole buildings and assemblies (Athena Institute2010). The Impact Estimator was the source of the embodied datafor the Toronto pilot.

3.6. FileMaker Pro databaseThe Python coding language and UNIX terminal interface were

highly useful tools for generating and manipulating the millionsof files and data involved with large-scale simulation, but was notconsidered efficient at generating visual representation or resultsanalysis of massive data sets in the context of this preliminarystudy. All relevant input and results parameters were written intoa tab-delimited text file that was fed into FileMaker Pro to create adatabase. This database is a massive, dynamic spreadsheet, withcolumns for each important parameter, and 1 080 000 rows thatcontain the full definition and calculated performance of eachdesign scenario.

An initial database was built to include the input parametersand operational performance results from simulation. Additional

columns were added to include the selected embodied energyfactors and to calculate the total LEC of each unique situation.FileMaker Pro was used to generate the graphical results pre-sented in Section 5.

4. Detailed methodology and challengesencountered

This section provides an in-depth description of the methodol-ogy in addition to some of the challenges encountered. For a moredetailed explanation, including sample code, refer to Fix (2010).

4.1. Creation of the parametric simulation tool

4.1.1. Generating permutationsPython code was developed to generate all of the design permu-

tations to be evaluated. First, design parameters were defined,with a range of discrete values for each. Next, an array was popu-lated with the full scope of parameters and values. The specificparameter names and discrete values could be returned from thearray. Finally, a set of nested ‘for’ loops ran through each param-eter’s value list, and populated the permutation array with thepermutation number, followed by the unique combination of in-put values. This code was initially tested on a small set of param-eters, however, once this process is scaled up, the volume of databecome too great to manually validate in any way. When dealingwith large simulation sets, automated validation and inspectiontechniques are recommended.

4.1.2. EnergyPlus automationCode was written to find and open the appropriate EnergyPlus

executable files. Value-based manipulated simulation parameterswere simple to embed using Python’s string manipulation tools.However, the EnergyPlus geometry definition was much morecomplex. It was separated into another Python executable file thathoused all of the unique geometry code blocks required and em-bedded the various values that were fed into each block.

4.1.3. Parallelizing the simulationsThe total simulation set was split into 24 parallel simulation

groups. Because the average simulation times varied from 30 s to90 s depending on, for example, the geometry and (or) inclusion ofa basement, it was necessary to split the simulations in such amanner that each group would require approximate similar sim-ulation times (to reduce costs and overall simulation time). Tobalance the simulation groups, each permutation simulation filewas randomly assigned to one of the 24 groups. This was first,unsuccessfully, attempted by 24 separate Python executable fileswritten to concurrently pull out .IDF files from the same folder,simulate them, and write the output files into the same directory.This proved impossible, as EnergyPlus creates temporary files dur-

Table 1. Summary of the manipulated simulation parameters within the Toronto pilot.

Manipulated parameterNumberof values Selected values

Number of stories 6 1–3 stories with and without basementTotal floor area 5 92 m2, 139 m2, 186 m2, 325 m2, 465 m2

Building width to length ratio 3 0.75, 1, 1.25East and west window to wall ratio 3 10%, 20%, 30%South window to wall ratio 4 10%, 30%, 50%, 60%Window performance 5 U values of 5.4, 3.2, 1.8, 1.0, 0.48 (W·m−2·K−1)Ceiling insulation value 10 Thermal resistances of 2.5, 5, 7.5, 10, 12.5, 15,

17.5, 20, 22.5, 25 (m2K/W)Envelope infiltration 5 10, 5, 2.5, 1, 0.25 ACH@50 PaEnvelope concrete thickness (thermal mass) 4 300 mm, 150 mm, 50 mm, 0.01 mmTotal # permutations 1 080 000

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ing simulation, which conflict when multiple EnergyPlus execu-tions run concurrently from the same directory. To solve this,each EnergyPlus execution had to have its own ‘silo’ to workwithin. A text file was created that contained a line-by-line list ofthe randomized permutation names. This file was copied intoeach of the 24 EnergyPlus processing silos, as a master dictionaryof all the input file names. Further research into the Python pro-gramming language following this study informed the authorsthat modules within Python would avoid this error and a multi-processing module provides support to isolate the different pro-cesses. In summary, parallelizing the EnergyPlus simulations mayprovide an avenue to maximize simulation time reduction, how-ever, the benefits of using a parametric pre-processing program,such as jEPlus, to organize simulation logic require further re-search in the context of this work.

4.2. Developing an AMITo use Amazon’s elastic cloud computing (EC2), a user first

creates virtual computers, called instances, by either designingtheir own or using an available Amazon Machine Image (AMI) todefine the basic setup. An AMI varies by its instance type, operat-ing system, and by the software and data that has been installedon it. For supercomputing applications like the Toronto pilot,three identical instances of type c1.xlarge, were created using abasic 64 bit LINUX-based AMI that is freely available from Amazon(for more details on instance types refer to Amazon Web Services2010c). It was necessary for the user to install any required soft-ware on each AMI, as they only came with the selected operatingsystem.

Interacting with a LINUX-based Amazon instance was mostly atext-based task, as few graphic interfaces exist. The Firefox opensource web browser, has a highly useful and free add-on calledElasticFox, which provides a basic graphical interface for manag-ing Amazon Instances, AMI’s, and Elastic Block Storage (EBS) vol-umes (Amazon Web Services 2010d). Once connected to a LINUXinstance, the interaction was solely through a terminal windowconnection, which required knowledge of the UNIX commandlanguage. This terminal interface was quite logical and highlyefficient in manipulating large processes and data volumes.

During the Toronto pilot development, a single copy commandwithin a sequence was misnamed, which went unnoticed untilfull-scale simulations were well underway. The result of the copyerror was that the Run5.py and Run6.py execution files both hadthe contents of Run5.py, although they were named correctly.When simulations began, both executables attempted to feed thesame range of input .IDF files into simulation, concurrently. Thetwo processes managed to run through approximately 25 000 .IDFfiles before crashing, at which point the problem became appar-ent. Even after the processes were restarted, this simple copymistake caused a cascading chain of garbage files and clutterthroughout the rest of the process, until the final database wasbuilt.

4.3. Simulating with the cloudThroughout the process of managing multiple instances, it can

be cumbersome to transfer large amounts of data between a localmachine and the Amazon instance. A possible solution to this is totransfer information between Amazon instances, using Amazon’sS3 storage as an intermediate transfer point. S3 stands for ‘SimpleStorage Solution’, and is a service that allows for the storage oflarge amounts of data within an online cloud that can be accessedvia the web. The difference between an Amazon Elastic BlockStorage (EBS) volume and an S3 bucket is the speed of accessibil-ity; an EBS volume is designed to act as a hard drive connected toAmazon instances, while a S3 bucket is meant for cheap, reliable,long-term storage of data. To interact with S3 from an Amazoninstance, a Ruby programming language package named sc3sync

has been developed and is freely available (Amazon Web Services2007). Once the necessary start up UNIX commands are issued oneach AMI, the “TOP” command can be used provided visual veri-fication that all of the EnergyPlus processes are running success-fully.

The process has real-time internal compression, so the total700 GB of data was reduced to 120 GB of data transfer, takingapproximately three days to download to the authors’ externalhard drive using the authors’ residential internet connection.

4.4. Quantifying embodied energy

4.4.1. Insulation embodied energySix different thermal insulation types were included in the To-

ronto pilot: blown cellulose, expanded polystyrene board, fiber-glass batt, polyisocyanurate board, Rockwool batt, and extrudedpolystyrene board. For each insulation type, a 1 m × 1 m × 25.4 mmvolume was modeled with the Athena Impact Estimator (AthenaInstitute 2010). From the total embodied energy calculated for thisvolume, a unit embodied energy value was calculated with theunits of kWh/m3 insulation. This value was then multiplied by thevolume of insulation present in each design to obtain a total levelof embodied energy.

4.4.2. Window embodied energyThe Athena Impact Estimator does not currently house a large

window database, so the embodied energy of all Toronto pilotwindow constructions, was calculated by scaling the impacts oftwo double glaze constructions. An expert in life cycle analysiswith in depth knowledge of the ATHENA SMI database verifiedthis method as accurate within the error tolerances of the ImpactEstimator (personal communication 2010).

4.4.3. Thermal mass embodied energyIn the Toronto pilot, thermal mass was modeled as a variable

thickness of concrete on the interior side of thermal insulation,on all exposed surfaces. The embodied environmental factors for1 m3 concrete were calculated using Athena’s Impact Estimator.The four thicknesses of this layer, 0.01 mm, 50 mm, 150 mm, and300 mm are meant to approximately emulate the thermal mas-siveness of four building types varying from lightweight to ther-mally massive. Converted to appropriate units, the embodiedenergy of concrete is 482.8 kWh/m3. This value is multiplied bythe volume of the thermal mass layer within each Toronto pilotdesign to calculate the total embodied energy within that layer.

4.4.4. Fuel source embodied energyThe embodied environmental effect of the operational energy

consumed by each building design was included in the Torontopilot analysis. Using data from the Athena Impact Estimator, theimpacts of consuming electricity and natural gas in Ontario werecalculated; the consumption of 1 kWh site energy results in 2.03 kWhof primary energy consumption.

5. Results and discussionThe LEC of all 1 080 000 design scenarios was reported and an-

alyzed using two characterizations: (1) total LEC and (2) the leastand greatest 1% LEC groups. It should be noted that many morepertinent characterizations of the data set exist and require anal-ysis as part of future work or other research projects utilizingcloud computing to conduct mass simulation. The purpose of thisresearch was to show the use of cloud computing to carry outlarge-scale simulation is possible within a reasonable budget, re-duced simulation time, and to show preliminary uses for the gener-ated data set. As such, the authors chose the four characterizations as

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a sample output, based on the ability to substantiate purpose and tohighlight logistical issues.

5.1. Using cloud computing to support large simulationsets

With respect to reducing simulation time, the results haveshown a reduction of 95% with respect to simulation time which iscomparable to the literature, however this study generally con-ducts 2 to 3 times more designs than previous cited studies. Thisrepresents the estimated 563 days of simulation time on a stan-dard personal machine reduced to 28 days utilizing the method-

ology outlined in this paper. The impact of this reduction in timeis important for both this and future projects utilizing cloud com-puting to complete large-scale simulation sets.

For the Toronto pilot, the significant reduction in simulationtime equates to an increased potential to add additional manipu-lated parameters. At the outset of this research, the authors wereconstrained by a lack of quantitative understanding in regards tothe simulation times utilizing cloud computing. As a result, theincluded manipulated parameters discussed above were limited,thus reducing the rigor of this particular preliminary pilot study as itpertains to informing building design. With the added knowledge of

Fig. 1. Annual operational primary energy and embodied for cellulose insulation cases.

Fig. 2. Annual operational primary energy and embodied for XPS insulation cases.

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reduced simulation time from this pilot study, this and other re-search projects utilizing cloud computing to conduct large-scale sim-ulation are better informed to optimize the dynamic variable set.Knowledge of simulation time thus informs optimal variablechoice and provides flexibility during the mass-simulation designphase of the research.

It can be further concluded that using cloud computing forlarge-scale simulation sets is relatively easy, as the authors hadbasic programming skills prior to this project. However, signifi-

cant learning was required particularly with the Python and Linuxcommands, thus it is expected that organizing a cloud computingbased project quickly, requires considerable computer skills orprior experience.

Although, theoretically, simulation times could essentially bereduced to almost zero with a large enough number of AMIs, theset up time required increases with the number of AMIs used. Thesimulations become more complex to control when more AMIsare used, as the process is more fragmented. Hence, monitoring to

Fig. 3. Annual operational energy, embodied energy, and 50 year lifetime consumption of primary energy using electric heat and cooling forcellulose insulation cases.

Fig. 4. Annual operational energy, embodied energy, and 50 year lifetime consumption of primary energy using electric heat and cooling forXPS insulation cases.

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ensure all processes continue to run error-free becomes increasinglytime consuming as the number of AMIs increase. Therefore, theappropriate number will depend on the specifics of the project,such as the number of simulations, the total acceptable simula-tion time, and the computer skills of the simulationist.

Because the number of permutations grows exponentially asadditional parameters are added, there is a limit to the use of thistechnique when searching for an optimal solution. Furthermore,with a large number of permutations processing the results can bedifficult and time consuming.

5.2. Lifetime energy consumption (LEC)This analysis first focuses on the relative magnitudes of embod-

ied, annual operational, and LEC, across all 1 080 000 Torontopilot design scenarios. To investigate the trend between extremedesign parameters, cellulose insulation and electric heating werecoupled to represent a minimal embodied energy case while ex-truded polystyrene insulation (XPS) and natural gas heating werecoupled to represent a high embodied energy case. Figure 1 com-pares embodied energy with annual operational energy, for allbuildings using cellulose insulation and electric heating. Figure 2shows the same comparison using XPS insulation and natural gasheating on all buildings. In Fig. 1, the trends show that a building

design with cellulose insulation and electric heating will gener-ally have annual operational energy consumption and embodiedenergy of the same magnitude. However, in Fig. 2, it appears thata building with XPS insulation generally has greater embodiedenergy than its annual operational energy. Since annual opera-tional energy is consumed every year over the building’s lifetime,and the embodied is generally only consumed once (barring ren-ovation), operational energy still tends to dominant a building’sLEC in all Toronto pilot cases. Figure 3 and Fig. 4 show the 50-yearenergy consumption of the Toronto pilot designs, with electricheat and cellulose insulation (Fig. 3), and with gas heat and ex-truded polystyrene insulation (Fig. 4), respectively, and the trendsagree that operational energy dominates LEC. This research cor-relates with existing research (PHIUS 2010) that operational en-ergy dominates the LEC of a building. This appears to be the caseregardless of heating source of insulation embodied energy. It isalso apparent that choosing XPS over cellulose insulation willresult in a greater lifetime consumption of LEC — perhaps a coun-ter intuitive result for many since it is commonly regarded thatXPS has a higher thermal resistance than cellulose. It is problem-atic for lay people to discern between normalized material prop-erties and overall specified thermal resistance levels.

Fig. 5. Frequency of occurrence of design parameter values within the least 1% LEC group.

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Note that there is no visible relationship between annualoperational energy and embodied energy in Fig. 1 or Fig. 2.There is also no dominant trend visible between LEC and em-bodied energy in Fig. 3 or Fig. 4. This is unexpected, as theincreases in embodied window, insulation, and thermal massenergy are a direct result of what are generally considered as‘Energy Saving Design Measures’. These example results show

that the magnitude of embodied energy in itself is not a goodpredictor of operational energy consumption or LEC; other pa-rameters must be dominant. Although for this preliminarystudy, the effect of embodied energy on LEC appears to bemarginal, other work (Bowick et al. 2010; Bowick 2011) showsscenarios where embodied effects are substantial and shouldcontinue to be included in future research.

Fig. 6. Frequency of occurrence of design parameter values within the greatest 1% LEC group.

Fig. 7. Two designs with identical design parameters, one with basement, one without.

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5.3. Top 1% and worst 1% LEC scenariosThe frequency of occurrence of each parameter across its entire

range within the top 1% LEC group (least energy consumed) isshown in Fig. 5. The concentration of single values from basementinclusion, total area, window performance, and infiltration aredominant. Of these top performing 1% designs, the following im-portant trends were observed:

• 87.3% included a basement to increase sub-grade area, and only12.7% did not.

• 83.6% of the top 1% LEC had a total floor area of 92.9 m2 (thesmallest in the pilot scope).

• 77.6% had the highest performance, quadruple glazed win-dows.

• 62.9% had lowest infiltration level of 0.25ACH@50 Pa.

Contrary to the author’s expectations, the occurrence frequency ofinsulation values, thermal mass values, and south window to wallratio are elementally balanced. The results imply the most effectiveway to minimize LEC is to include a portion of the building areabelow grade, make the building as small as possible, use the high-est performance windows, and use the most air-tight constructiontechniques possible. This type of trend analysis can hold greatvalue to the building industry and large-scale simulation providesa vehicle to complete it.

The parameter value occurrence frequency of the highest1% LEC group is shown in Fig. 6. The results are similar to those ofthe top 1% group; certain values of infiltration, window perfor-mance, total building area, east–west glazing area, and basementhave the most frequent occurrence.

These results bolster the idea that the influence of certain pa-rameters, namely basement, total area, infiltration, window per-formance, and east–west window area, dominate the overall LECof a building — thus having an equal effect on making a design asuccess or, consequently, a failure. This is not to suggest thatparameters such as insulation level and south window area areirrelevant, but that perhaps the more dominant parametersshould be addressed first in an optimized design process. Notethat these results do not suggest that simply adding a basement toan existing home design will result in a reduction of LEC. Figure 7elaborates on the definition of geometry within the Toronto pilot.Basement area is included in the building’s total area as defined inthe Toronto pilot. Figure 7 shows a 100 m2 single floor home anda 100 m2 single floor home with basement. It is highly interestingthat simple geometry changes like this appear to have the greatestimpact on LEC — the idea of compactness ratio appears to be astrong variable for achieving minimal LEC.

6. ConclusionThis work presents a method that can be used to achieve great

reductions in simulation run times, allowing for large-scale para-metric analysis and optimization projects, and reduced feedbacktimes during design stage analysis. The benefits of using cloudcomputing have been demonstrated through the successful exe-cution of the Toronto pilot, a LEC parametric study including1 080 000 single detached home designs for CAD $2400.

Within the scope of lifetime energy consumption (LEC) ofsingled detached homes in Toronto, large-scale simulation withcloud computing has produced a framework for utilizing resultsto show how design parameters like total building area, basementinclusion, window performance, and infiltration level have themost dominant influence on reducing LEC. This preliminary evi-dence suggests that low energy building design should begin withminimizing building size, minimizing air-exposed surface area,maximizing window performance, and maximizing air tightness.Once these more dominant parameters have been defined, thenthe designer should proceed with selecting other parameters suchas glazing ratios, insulation levels, etc.

The results also show that there is no single, discretely optimaldesign from the perspective of LEC. For example, there is no fixedfloor area with minimal LEC. Instead, the results begin to high-light trends, and suggest that there is likely an optimal order thatdesign decisions should be made; those parameters that havemost influence on LEC should be singled out and optimized first.A ‘best practice’ guide or tool could be developed to aid designersin minimizing their designs’ LEC.

Using Amazon’s cloud computing services required minimalcomputer programming skills to set up, and enabled a reductionof simulation time by a factor of approximately 19. Although thismethod is likely not the best use of computational resources whensearching for an optimal solution, it shows promise for large-scaleparametric analysis studies and for verifying optimization algo-rithms.

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