Modeling Human Behavior in Building Performance Simulation: Gaussian Process and Monte Carlo Approach to the Energy Simulation of Residential Buildings

Abstract

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.