  This paper demonstrates a data-driven control approach for demand response in
real-life residential buildings. The objective is to optimally schedule the
heating cycles of the Domestic Hot Water (DHW) buffer to maximize the
self-consumption of the local photovoltaic (PV) production. A model-based
reinforcement learning technique is used to tackle the underlying sequential
decision-making problem. The proposed algorithm learns the stochastic occupant
behavior, predicts the PV production and takes into account the dynamics of the
system. A real-life experiment with six residential buildings is performed
using this algorithm. The results show that the self-consumption of the PV
production is significantly increased, compared to the default thermostat
control.
