A Supervised-Learning-Based Strategy for Optimal Demand Response of an HVAC System in a Multi-Zone Office Building

Abstract

The 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. Using supervised learning (SL), this paper proposes a new strategy for optimal DR of an HVAC system in a multi-zone building. 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 optimization problem for price-based DR. The problem is solved for various electricity prices and building thermal conditions. The solutions are used to train a deep neural network (DNN) to directly determine the optimal DR schedule, which is termed as SL-aided meta-prediction (SLAMP) here; the DNN can work as a price-and-optimal-demand curve. Case studies are performed using three different methods: explicit ANN replication, SLAMP, and physics-based modeling. The case study results verify that the proposed SL-based strategy is effective 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.