02296nas a2200193   4500008003900000245011200039210006900151520161300220653002401833653002401857653002201881653002201903653002301925653002401948100001501972700001901987700001502006856008102021       2014                          d00aStochastic Modeling of Overtime Occupancy and Its Application in Building Energy Simulation and Calibration0 aStochastic Modeling of Overtime Occupancy and Its Application in3 a<p>Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.</p>10abuilding energy use10abuilding simulation10amodel calibration10aoccupant behavior10aovertime occupancy10astochastic modeling1 aSun, Kaiyu1 aHong, Tianzhen1 aGuo, Siyue  uhttps://simulationresearch.lbl.gov/publications/stochastic-modeling-overtime