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

Samuel Moveh (moveh.s@tsi.lv), Emmanuel Alejandro Merchán-Cruz, Ahmed Osman Ibrahim, Zeinab Abdallah Mohammed Elhassan, Nada Mohamed Ramadan Abdelhai and Mona Dafalla Abdelrazig
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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

Sustainability, 2025, vol. 17, issue 5, 1-28

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.

Keywords: deep reinforcement learning; EnergyPlus; HVAC system; Jeddah; model predictive control; support vector regression; linear kernel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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