Innovative Energy Efficiency in HVAC Systems with an Integrated Machine Learning and Model Predictive Control Technique: A Prospective Toward Sustainable Buildings

Khaled Almazam, Omar Humaidan (), Nahla M. Shannan, Faizah Mohammed Bashir, Taha Gammoudi and Yakubu Aminu Dodo ()
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Khaled Almazam: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia
Omar Humaidan: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia
Nahla M. Shannan: Program in Control & Automation Engineering Technology, Applied College, University of Ha’il, Ha’il 55476, Saudi Arabia
Faizah Mohammed Bashir: Department of Decoration and Interior Design Engineering, College of Engineering, University of Ha’il, Ha’il 55476, Saudi Arabia
Taha Gammoudi: Department of Fine Arts, College of Letters and Arts, University of Ha’il, Ha’il 55476, Saudi Arabia
Yakubu Aminu Dodo: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia

Sustainability, 2025, vol. 17, issue 7, 1-35

Abstract: This study introduces a novel approach, combining radial basis function neural network (RBFNN) and model predictive control (MPC) techniques to enhance energy efficiency in HVAC systems for sustainable buildings. The proposed methodology is evaluated in a single-story commercial and residential building in Najran, Saudi Arabia, utilizing new input parameters such as ambient temperature, cooling load, and compressor speed, alongside output metrics including room temperature and total exergy destruction and coefficient of performance (CoP) of the HVAC system. Significant improvements in energy management practices were observed, with a reduction in energy consumption by approximately 15% compared to conventional control models. The model’s predictive capabilities were validated against real-world electricity consumption data, demonstrating a high correlation with discrepancies ranging from 0.2% to 2.5%. Furthermore, the integration of machine learning techniques enabled more precise control of HVAC operations, addressing concerns regarding the system’s dynamic behavior and optimizing performance under varying occupancy patterns. While in the commercial building, the model achieves RMSE and CV values of approximately 1.0 and 0.61 for room temperature, 1.21 and 0.48 for exergy destruction, and 0.65 and 0.30 for CoP. However, for the residential building, RMSE and CV values are approximately 0.95 and 0.69 for room temperature, 1.08 and 0.31 for exergy destruction, and 0.55 and 0.27 for CoP.

Keywords: machine learning; HVAC system; radial basis function neural network; model predictive control; building (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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