Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance

TitleAccelerating the energy retrofit of commercial buildings using a database of energy efficiency performance
Publication TypeJournal Article
Year of Publication2015
AuthorsLee, Sang Hoon, Tianzhen Hong, Mary Ann Piette, Geof Sawaya, Yixing Chen, and Sarah C. Taylor-Lange
Start Page738
Date Published07/2015
Keywordsbuilding simulation, Energy conservation measure, energy modeling, energyplus, High Performance computing, retrofit

Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use,
however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed
on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a
DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit
analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus
simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The
simulations utilize prototype models representative of small and mid-size offices and retails in California
climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance
computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope,
lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and
simulation environment to create DEEP is flexible and can expand to cover additional building types,
additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the
Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by
querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial

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