  The residential buildings sector is one of the largest electricity consumers
worldwide and contributes disproportionally to peak electricity demand in many
regions. Strongly driven by occupant activities at home, household energy
consumption is stochastic and heterogeneous in nature. However, most
residential building energy models applied by industry use homogeneous,
deterministic occupant activity schedules, which work well for predictions of
annual energy consumption, but can result in unrealistic hourly or sub-hourly
electric load profiles, with exaggerated or muted peaks. This mattered less in
the past, but the increasing proportion of variable renewable energy generators
in power systems means that representing the heterogeneity and stochasticity of
occupant behavior is crucial for reliable energy planning. This is particularly
true for systems that include distributed energy resources, such as
grid-interactive efficient buildings, solar photovoltaics, and battery storage.
This work presents a stochastic occupant behavior simulator that models the
energy use behavior of individual household members. It also presents an
integration with a building stock model to simulate residential building loads
more accurately at community, city, state, and national scales. More
specifically, we first employ clustering techniques to identify distinct
patterns of occupant behavior. Then, we combine time-inhomogeneous Markov chain
simulations with probabilistic sampling of event durations to realistically
simulate occupant behaviors. This stochastic simulator is integrated with
ResStock, a large-scale residential building stock simulation tool, to
demonstrate the capability of stochastic residential building load modeling at
scale. The simulation results were validated against both American Time Use
Survey data and measured end-use electricity data for accuracy and reliability.
