Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/22/5607 |
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| author | Minghui Ma Oguzhan Pektezel Vincenzo Ballerini Paolo Valdiserri Eugenia Rossi di Schio |
| author_facet | Minghui Ma Oguzhan Pektezel Vincenzo Ballerini Paolo Valdiserri Eugenia Rossi di Schio |
| author_sort | Minghui Ma |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bcf42fd63c4476da5a55db6a9b3e0f9 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9bcf42fd63c4476da5a55db6a9b3e0f92024-11-26T18:02:01ZengMDPI AGEnergies1996-10732024-11-011722560710.3390/en17225607Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence MethodsMinghui Ma0Oguzhan Pektezel1Vincenzo Ballerini2Paolo Valdiserri3Eugenia Rossi di Schio4Department of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Mechanical Engineering, University of Tokat Gaziosmanpasa, Tokat 60250, TurkeyDepartment of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering DIN, Alma Mater Studiorum—University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyThe 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.https://www.mdpi.com/1996-1073/17/22/5607data-driven intelligent algorithmsprediction modelsMLPSVMRFsolar-assisted heat pumps |
| spellingShingle | Minghui Ma Oguzhan Pektezel Vincenzo Ballerini Paolo Valdiserri Eugenia Rossi di Schio Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods Energies data-driven intelligent algorithms prediction models MLP SVM RF solar-assisted heat pumps |
| title | Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods |
| title_full | Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods |
| title_fullStr | Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods |
| title_full_unstemmed | Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods |
| title_short | Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods |
| title_sort | performance predictions of solar assisted heat pumps methodological approach and comparison between various artificial intelligence methods |
| topic | data-driven intelligent algorithms prediction models MLP SVM RF solar-assisted heat pumps |
| url | https://www.mdpi.com/1996-1073/17/22/5607 |
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