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Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) systems in multizone buildings

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  • Razzano, Giuseppe
  • Brandi, Silvio
  • Piscitelli, Marco Savino
  • Capozzoli, Alfonso

Abstract

The paper introduces a novel methodology for optimizing the operation of a centralized Air Handling Unit (AHU) in a multi-zone building served by VAV boxes with interpretable rules extracted from a Deep Reinforcement Learning (DRL) controller trained to enhance energy efficiency and indoor temperature control. To ensure practical application, a Rule Extraction (RE) framework is developed, translating the DRL complex decision-making process into actionable rules using decision trees. A multi-action approach is proposed by developing three different regression trees for adjusting the supply water temperature, the position of the chiller valve, and the position of the economizer damper of the AHU. The extracted rules are benchmarked against the original DRL controller and two conventional control sequences based on ASHRAE 2006 and ASHRAE Guideline 36 within a high-fidelity co-simulation architecture combining Spawn of EnergyPlus and Python. The co-simulation environment uses EnergyPlus for building envelope and loads while HVAC components and controls are implemented in the equation-based modeling language Modelica. Results show that the RE-based controller closely approximates the performance of the DRL policy with an electric energy consumption only 3% higher, highlighting its ability to effectively mirror a more complex control logic, representing a transparent and easily implementable alternative. The controllers based on ASHRAE 2006 and ASHRAE Guideline 36 lead to higher energy consumption (for both chiller and fan) and violations of indoor temperature compared to both RE-based control and DRL. This study underscores the potential of integrating AI-driven control methods with interpretable rule-based systems, facilitating the adoption of advanced energy management strategies in real-world building automation systems.

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  • Razzano, Giuseppe & Brandi, Silvio & Piscitelli, Marco Savino & Capozzoli, Alfonso, 2025. "Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) syste," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024309
    DOI: 10.1016/j.apenergy.2024.125046
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    References listed on IDEAS

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    1. Mu, Yuanpeng & Zhang, Jili & Ma, Zhixian & Liu, Mingsheng, 2023. "A novel air flowrate control method based on terminal damper opening prediction in multi-zone VAV system," Energy, Elsevier, vol. 263(PD).
    2. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    3. Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).
    4. Yu, Min Gyung & Pavlak, Gregory S., 2022. "Extracting interpretable building control rules from multi-objective model predictive control data sets," Energy, Elsevier, vol. 240(C).
    5. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    6. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    7. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
    8. Fu, Yangyang & Xu, Shichao & Zhu, Qi & O’Neill, Zheng & Adetola, Veronica, 2023. "How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system," Energy, Elsevier, vol. 273(C).
    9. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    10. Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).
    11. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    12. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
    13. Liu, Mingzhe & Guo, Mingyue & Fu, Yangyang & O’Neill, Zheng & Gao, Yuan, 2024. "Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications," Applied Energy, Elsevier, vol. 372(C).
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