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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models

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  • Mohamed Salah Benkhalfallah

    (Department of Mathematics and Computer Science, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria
    Artificial Intelligence and Autonomous Things Laboratory, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria)

  • Sofia Kouah

    (Department of Mathematics and Computer Science, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria
    Artificial Intelligence and Autonomous Things Laboratory, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria)

  • Saad Harous

    (College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates)

Abstract

Optimization of energy consumption in urban infrastructures is essential to achieve sustainability and reduce environmental impacts. In particular, accurate regression-based energy forecasting of the energy consumption in various sectors plays a key role in informed decision-making, efficiency improvements, and resource allocation. This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. By evaluating performance of several regression algorithms using various metrics, this study identifies the most effective method for analyzing sectoral energy consumption. The results show that the Regression Tree Ensemble algorithm outperforms other techniques, achieving an accuracy of 97.00%, followed by Random Forest Regression (96.20%) and Gradient Boosted Regression Trees (95.50%). These results underline the potential of machine learning models to foster intelligent energy management and promote sustainable energy practices in smart cities.

Suggested Citation

  • Mohamed Salah Benkhalfallah & Sofia Kouah & Saad Harous, 2025. "Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models," Energies, MDPI, vol. 18(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3672-:d:1699565
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    References listed on IDEAS

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