IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i14p3672-d1699565.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/14/3672/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/14/3672/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fritz Schiltz & Chiara Masci & Tommaso Agasisti & Daniel Horn, 2018. "Using regression tree ensembles to model interaction effects: a graphical approach," Applied Economics, Taylor & Francis Journals, vol. 50(58), pages 6341-6354, December.
    2. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    3. Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    4. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    5. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    6. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    7. Sijun Xu & Hua Zhang & Zilong Wang, 2023. "Thermal Management and Energy Consumption in Air, Liquid, and Free Cooling Systems for Data Centers: A Review," Energies, MDPI, vol. 16(3), pages 1-25, January.
    8. Yamile Díaz Torres & Paride Gullo & Hernán Hernández Herrera & Migdalia Torres del Toro & Mario A. Álvarez Guerra & Jorge Iván Silva Ortega & Arne Speerforck, 2022. "Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    9. Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    10. Li, Xuetao & Wang, Ziwei & Yang, Chengying & Bozkurt, Ayhan, 2024. "An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms," Energy, Elsevier, vol. 296(C).
    11. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kamran Hassanpouri Baesmat & Zeinab Farrokhi & Grzegorz Chmaj & Emma E. Regentova, 2025. "Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning," Forecasting, MDPI, vol. 7(2), pages 1-18, May.
    2. Wang, Peng & Sun, Junqing & Yoon, Sungmin & Zhao, Liang & Liang, Ruobing, 2024. "A global optimization method for data center air conditioning water systems based on predictive optimization control," Energy, Elsevier, vol. 295(C).
    3. Tehrani, Alireza Attarhay & Sobhaninia, Saeideh & Nikookar, Niloofar & Levinson, Ronnen & Sailor, David J. & Amaripadath, Deepak, 2025. "Data-driven approach to estimate urban heat island impacts on building energy consumption," Energy, Elsevier, vol. 316(C).
    4. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    5. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    6. Amir Shahcheraghian & Adrian Ilinca, 2024. "Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables," Energies, MDPI, vol. 17(18), pages 1-22, September.
    7. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    8. Sami Kabir & Mohammad Shahadat Hossain & Karl Andersson, 2024. "An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings," Energies, MDPI, vol. 17(8), pages 1-18, April.
    9. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    10. Niraj Buyo & Akbar Sheikh-Akbari & Farrukh Saleem, 2025. "An Ensemble Approach to Predict a Sustainable Energy Plan for London Households," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
    11. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    12. Nafeesa Javed & Muhammad Javaid Iqbal & Sohail Masood & Laiba Rehman & Saba Ramzan, 2024. "The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 777-783.
    13. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    14. Feng, Yiwei & Li, Yanpeng & Qu, Shengli & Liu, Yishuang & Wang, Chuang & Han, Yaoxiang & Xing, Ziwen, 2025. "Proactive operational strategy of thermal energy storage tank in an industrial multi-chiller system based on chilled water flow difference between supply and demand sides," Energy, Elsevier, vol. 319(C).
    15. Ssembatya, Martin & Baltazar, Juan-Carlos & Claridge, David E., 2025. "Evaluating inverse modeling methods for measurement and verification of chiller energy efficiency measures," Applied Energy, Elsevier, vol. 378(PA).
    16. Marian B. Gorzałczany & Filip Rudziński, 2024. "Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization," Energies, MDPI, vol. 17(13), pages 1-24, July.
    17. Sheng Du & Li Jin & Zixin Huang & Xiongbo Wan, 2025. "Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes," Energies, MDPI, vol. 18(8), pages 1-6, April.
    18. Zaher Abusaq & Sadaf Zahoor & Muhammad Salman Habib & Mudassar Rehman & Jawad Mahmood & Mohammad Kanan & Ray Tahir Mushtaq, 2023. "Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study," Energies, MDPI, vol. 16(4), pages 1-15, February.
    19. Tiezhu Sun & Xiaojun Huang & Caihang Liang & Riming Liu & Yongcheng Yan, 2023. "Energy Consumption and Energy Saving Analysis of Air-Conditioning Systems of Data Centers in Typical Cities in China," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    20. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3672-:d:1699565. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.