  This paper presents a new approach to predict the occupancy for building
energy systems (BES). A Gaussian Process (GP) is used to model the occupancy
and is represented as a state space model that is equivalent to the full GP if
Kalman filtering and smoothing is used. The combination of GPs and mechanistic
models is called Latent Force Model (LFM). An LFM-based model predictive
control (MPC) concept for BES is presented that benefits from the extrapolation
capability of mechanistic models and the learning ability of GPs to predict the
occupancy within the building. Simulations with EnergyPlus and a comparison
with real-world data from the Bosch Research Campus in Renningen show that a
reduced energy demand and thermal discomfort can be obtained with the LFM-based
MPC scheme by accounting for the predicted stochastic occupancy.
