Authors: Hwang Yi
Occupant behavior has a large impact on residential building energy use; nevertheless, behavior models in building energy simulation need scrutiny of investigation and use of oversimplified schedules results in a great deal of uncertainty in building energy prediction; the modeling of accurate occupant schedules requires a complex dataset with long-term observations, which is concealed during early stages of building projects. This study seeks to estimate behavior-related building operation schedules based on a minimum number of observations, so that energy simulation is effectively involved in the early stages of building projects. To this end, Gaussian process (GP) regression is applied to modeling five major occupant activities (space occupancy, activity level, hot water use, appliance use, and lighting control) of a single-family house in the United State. Monte Carlo simulation with sampling from GP-based occupant schedules demonstrates large variability of energy simulation results according to different human behaviors.
Keywords: component; occupant behavior; building energy simulation; Gaussian process; Monte Carlo Simulation