A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University

TL;DR

This paper introduces a reinforcement learning framework for optimizing campus chiller energy management, significantly reducing electricity consumption in a university's HVAC system through predictive modeling and receding horizon control.

Contribution

It develops a novel RL-based control method with a receding horizon framework and a priority reward function for constraint satisfaction, improving energy efficiency over traditional rule-based controls.

Findings

Achieves up to 28% electricity savings.

Develops a predictive cooling demand model.

Validates the RL approach in a real university campus case.

Abstract

This work presents a case study of optimal energy management of a large Heating Ventilation and Cooling (HVAC) system within a university campus in Australia using Reinforcement Learning (RL). The HVAC system supplies to nine university buildings with an annual average electricity consumption of $\sim2$ GWh. Updated chiller Coefficient of Performance (COP) curves are identified, and a predictive building cooling demand model is developed using historical data from the HVAC system. Based on these inputs, a Proximal Policy Optimization based RL model is trained to optimally schedule the chillers in a receding horizon control framework with a priority reward function for constraint satisfaction. Compared to the traditional way of controlling the HVAC system based on a reactive rule-based method, the proposed controller saves up to 28\% of the electricity consumed by simply controlling the mass flow rates of the chiller banks and with minimal constraint violations.