@article {60962,
	title = {Advances in research and applications of energy-related occupant behavior in buildings},
	journal = {Energy and Buildings},
	volume = {116},
	year = {2016},
	month = {03/2016},
	pages = {694-702},
	abstract = {<p>Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead.</p>},
	keywords = {Behavior Modeling, Building design and operation, building performance simulation, energy use, occupant behavior},
	doi = {10.1016/j.enbuild.2015.11.052},
	author = {Tianzhen Hong and Sarah C. Taylor-Lange and Simona D{\textquoteright}Oca and Da Yan and Stefano P. Corgnati}
}
@article {60965,
	title = {Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance},
	journal = {Energy},
	year = {2015},
	month = {07/2015},
	chapter = {738},
	abstract = {<p>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.</p>},
	keywords = {building simulation, Energy conservation measure, energy modeling, energyplus, High Performance computing, retrofit},
	doi = {10.1016/j.energy.2015.07.107},
	author = {Sang Hoon Lee and Tianzhen Hong and Mary Ann Piette and Geof Sawaya and Yixing Chen and Sarah C. Taylor-Lange}
}
