A supervised-learning-based strategy for optimal demand response of an HVAC System

TL;DR

This paper introduces a supervised learning strategy for optimizing demand response in multi-zone HVAC systems, combining neural networks and physics-based models to improve efficiency and thermal comfort.

Contribution

It presents a novel supervised learning approach that integrates neural networks with physics-based models for optimal HVAC demand response in multi-zone buildings.

Findings

SLAMP effectively determines optimal DR schedules.

The proposed method reduces computational time.

Thermal comfort and cost savings are maintained.

Abstract

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.