TY - JOUR T1 - Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance JF - Energy Y1 - 2015 A1 - Sang Hoon Lee A1 - Tianzhen Hong A1 - Mary Ann Piette A1 - Geof Sawaya A1 - Yixing Chen A1 - Sarah C. Taylor-Lange KW - building simulation KW - Energy conservation measure KW - energy modeling KW - energyplus KW - High Performance computing KW - retrofit AB -

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 payback.

U2 - LBNL-1004494 ER -