  This work presents a fully data-driven, black-box pipeline to obtain an
optimal control policy for a multi-loop building control problem based on
historical building and weather data, thus without the need for complex
physics-based modelling. We demonstrate the method for joint control of room
temperature and bidirectional EV charging to maximize the occupant thermal
comfort and energy savings while leaving enough energy in the EV battery for
the next trip. We modelled the room temperature with a recurrent neural network
and EV charging with a piece-wise linear function. Using these models as a
simulation environment, we applied a deep reinforcement learning (DRL)
algorithm to obtain an optimal control policy. The learnt policy achieves on
average 17% energy savings over the heating season and 19% better comfort
satisfaction than a standard RB room temperature controller. When a
bidirectional EV is additionally connected and a two-tariff electricity pricing
is applied, the MIMO DRL policy successfully leverages the battery and
decreases the overall cost of electricity compared to two standard RB
controllers, one controlling the room temperature and another controlling the
bidirectional EV (dis-)charging. Finally, we demonstrate a successful transfer
of the learnt DRL policy from simulation onto a real building, the DFAB HOUSE
at Empa Duebendorf in Switzerland, achieving up to 30% energy savings while
maintaining similar comfort levels compared to a conventional RB room
temperature controller over three weeks during the heating season.
