mlsanityData to 14 Oct 2026

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

arXiv 2511.14160 ↗First posted 18 Nov 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

3 of 15 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Controlling Commercial Cooling Systems Using Reinforcement Learningcited by 29
  3. 2021District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learningcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems
  2. 2024An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
  3. 2025Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
  4. 2024Experimental evaluation of offline reinforcement learning for HVAC control in buildings
  5. 2026Hierarchical Control Framework Integrating LLMs with RL for Decarbonized HVAC Operation
  6. 2026Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
  7. 2026Generalizing HVAC Control With Domain Randomized Reinforcement Learning
  8. 2025Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control