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

Minghui Ma, Oguzhan Pektezel, Vincenzo Ballerini, Paolo Valdiserri () and Eugenia Rossi di Schio ()
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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

Energies, 2024, vol. 17, issue 22, 1-16

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.

Keywords: data-driven intelligent algorithms; prediction models; MLP; SVM; RF; solar-assisted heat pumps; coefficient of performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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