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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.14160 |
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| _version_ | 1866908661980332032 |
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| author | Musgrave, Laura Bhattacharjee, Arnab Saha, Tapan Kumar |
| author_facet | Musgrave, Laura Bhattacharjee, Arnab Saha, Tapan Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University Musgrave, Laura Bhattacharjee, Arnab Saha, Tapan Kumar Systems and Control 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. |
| title | A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.14160 |