  The flexibility of electrical heating devices can help address the issues
arising from the growing presence of unpredictable renewable energy sources in
the energy system. In particular, heat pumps offer an effective solution by
employing smart control methods that adjust the heat pump's power output in
reaction to demand response signals. This paper combines imitation learning
based on an artificial neural network with an intelligent control approach for
heat pumps. We train the model using the output data of an optimization problem
to determine the optimal operation schedule of a heat pump. The objective is to
minimize the electricity cost with a time-variable electricity tariff while
keeping the building temperature within acceptable boundaries. We evaluate our
developed novel method, PSC-ANN, on various multi-family buildings with
differing insulation levels that utilize an underfloor heating system as
thermal storage. The results show that PSC-ANN outperforms a positively
evaluated intelligent control approach from the literature and a conventional
control approach. Further, our experiments reveal that a trained imitation
learning model for a specific building is also applicable to other similar
buildings without the need to train it again with new data. Our developed
approach also reduces the execution time compared to optimally solving the
corresponding optimization problem. PSC-ANN can be integrated into multiple
buildings, enabling them to better utilize renewable energy sources by
adjusting their electricity consumption in response to volatile external
signals.
