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Thermodynamic Optimization of Building HVAC Systems Through Dynamic Modeling and Advanced Machine Learning

Author

Listed:
  • Samuel Moveh

    (Engineering Faculty, Transport and Telecommunication Institute, 1019 Riga, Latvia)

  • Emmanuel Alejandro Merchán-Cruz

    (Engineering Faculty, Transport and Telecommunication Institute, 1019 Riga, Latvia)

  • Ahmed Osman Ibrahim

    (Department of Architectural Engineering, College of Engineering, University of Ha’il, Ha’il 55476, Saudi Arabia)

  • Zeinab Abdallah Mohammed Elhassan

    (College of Architecture and Design, Department of Architecture, Renewable Energy Lab (RELAB), Prince Sultan University, Riyadh 12435, Saudi Arabia)

  • Nada Mohamed Ramadan Abdelhai

    (Architecture Engineering Program, Faculty of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Mona Dafalla Abdelrazig

    (Department of Interior Design, College of Arts and Humanities, Jazan University, Jazan 45142, Saudi Arabia)

Abstract

This study enhances thermodynamic efficiency and demand response in an office building’s HVAC system using machine learning (ML) and model predictive control (MPC). This study, conducted in a simulated EnergyPlus 8.9 environment integrated with MATLAB (R2023a, 9.14), focuses on optimizing the HVAC system of an office building in Jeddah, Kingdom of Saudi Arabia. Support vector regression (SVR) and deep reinforcement learning (DRL) were selected for their regression accuracy and adaptability in dynamic environments, with exergy destruction analysis used to assess thermodynamic efficiency. The models, integrated with MPC, aimed to reduce exergy destruction and improve demand response. Simulations evaluated room temperature prediction, HVAC energy optimization, and energy cost reduction. The DRL model showed superior prediction accuracy, reducing energy costs by 21.75% while keeping indoor temperature increase minimal at 0.12 K. This simulation-based approach demonstrates the potential of combining ML and MPC to optimize HVAC energy use and support demand response programs effectively.

Suggested Citation

  • Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Ahmed Osman Ibrahim & Zeinab Abdallah Mohammed Elhassan & Nada Mohamed Ramadan Abdelhai & Mona Dafalla Abdelrazig, 2025. "Thermodynamic Optimization of Building HVAC Systems Through Dynamic Modeling and Advanced Machine Learning," Sustainability, MDPI, vol. 17(5), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1955-:d:1599138
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    References listed on IDEAS

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