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Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods

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  • Minghui Ma

    (Department of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

  • Oguzhan Pektezel

    (Department of Mechanical Engineering, University of Tokat Gaziosmanpasa, Tokat 60250, Turkey)

  • Vincenzo Ballerini

    (Department of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

  • Paolo Valdiserri

    (Department of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

  • Eugenia Rossi di Schio

    (Department of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

Abstract

The coefficient of performance (COP) is a crucial metric for evaluating the efficiency of heat pump systems. Real-time monitoring of heat pump system performance necessitates continuously collecting and processing data from various components utilizing multiple sensors and controllers. This process is inherently complex and presents significant challenges. In recent years, artificial intelligence (AI) models have increasingly been applied in refrigeration, heat pump, and air conditioning systems due to their capability to identify and analyze complex patterns and data relationships, demonstrating higher accuracy and reduced computation time. In this study, multilayer perceptron (MLP), support vector machines (SVM), and random forest (RF) are used to develop COP prediction models for solar-assisted heat pumps. By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. On the test set, the MAE was 2.35% and the RMSE was 3.84%. The modeling time for the RF model was 6.57 s.

Suggested Citation

  • Minghui Ma & Oguzhan Pektezel & Vincenzo Ballerini & Paolo Valdiserri & Eugenia Rossi di Schio, 2024. "Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods," Energies, MDPI, vol. 17(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5607-:d:1517470
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    References listed on IDEAS

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    1. Vincenzo Ballerini & Bernadetta Lubowicka & Paolo Valdiserri & Dorota Anna Krawczyk & Beata Sadowska & Maciej Kłopotowski & Eugenia Rossi di Schio, 2023. "The Energy Retrofit Impact in Public Buildings: A Numerical Cross-Check Supported by Real Consumption Data," Energies, MDPI, vol. 16(23), pages 1-21, November.
    2. Angelidis, O. & Ioannou, A. & Friedrich, D. & Thomson, A. & Falcone, G., 2023. "District heating and cooling networks with decentralised energy substations: Opportunities and barriers for holistic energy system decarbonisation," Energy, Elsevier, vol. 269(C).
    3. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    4. Maranghi, Florian & Gosselin, Louis & Raymond, Jasmin & Bourbonnais, Martin, 2023. "Modeling of solar-assisted ground-coupled heat pumps with or without batteries in remote high north communities," Renewable Energy, Elsevier, vol. 207(C), pages 484-498.
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