  The large thermal capacity of buildings enables heating, ventilating, and
air-conditioning (HVAC) systems to be exploited as demand response (DR)
resources. Optimal DR of HVAC units is challenging, particularly for multi-zone
buildings, because this requires detailed physics-based models of zonal
temperature variations for HVAC system operation and building thermal
conditions. This paper proposes a new strategy for optimal DR of an HVAC system
in a multi-zone building, based on supervised learning (SL). Artificial neural
networks (ANNs) are trained with data obtained under normal building operating
conditions. The ANNs are replicated using piecewise linear equations, which are
explicitly integrated into an optimal scheduling problem for price-based DR.
The optimization problem is solved for various electricity prices and building
thermal conditions. The solutions are further used to train a deep neural
network (DNN) to directly determine the optimal DR schedule, referred to here
as supervised-learning-aided meta-prediction (SLAMP). Case studies are
performed using three different methods: explicit ANN replication (EAR), SLAMP,
and physics-based modeling. The case study results verify the effectiveness of
the proposed SL-based strategy, in terms of both practical applicability and
computational time, while also ensuring the thermal comfort of occupants and
cost-effective operation of the HVAC system.
