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

arXiv: https://arxiv.org/abs/2511.14160 · HTML: https://mlsanity.com/paper/2511.14160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Controlling Commercial Cooling Systems Using Reinforcement Learning](https://mlsanity.com/paper/2211.07357/) (2022)
- [District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2112.10949/) (2021)

## Abstract (arXiv)

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 ~2 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.