A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization
When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxil...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7771 |
| _version_ | 1797592036502667264 |
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| author | Jihyun Shin Jinhyun Lee Younghum Cho |
| author_facet | Jihyun Shin Jinhyun Lee Younghum Cho |
| author_sort | Jihyun Shin |
| collection | DOAJ |
| description | When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future. |
| first_indexed | 2024-03-11T01:46:45Z |
| format | Article |
| id | doaj.art-11949c7a674442cd91c3195edeaf97f3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:45Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-11949c7a674442cd91c3195edeaf97f32023-11-18T16:11:12ZengMDPI AGApplied Sciences2076-34172023-06-011313777110.3390/app13137771A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters OptimizationJihyun Shin0Jinhyun Lee1Younghum Cho2Enertecunited, Busan 48059, Republic of KoreaInstitute of Industrial Technology, Yeungnam University, Gyeongsan 38541, Republic of KoreaSchool of Architecture, Yeungnam University, Gyeongsan 38541, Republic of KoreaWhen the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future.https://www.mdpi.com/2076-3417/13/13/7771hybrid geothermal heat pump systemartificial neural networksupport vector machinehyper-parametercoefficient of performance |
| spellingShingle | Jihyun Shin Jinhyun Lee Younghum Cho A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization Applied Sciences hybrid geothermal heat pump system artificial neural network support vector machine hyper-parameter coefficient of performance |
| title | A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization |
| title_full | A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization |
| title_fullStr | A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization |
| title_full_unstemmed | A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization |
| title_short | A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization |
| title_sort | cop prediction model of hybrid geothermal heat pump systems based on ann and svm with hyper parameters optimization |
| topic | hybrid geothermal heat pump system artificial neural network support vector machine hyper-parameter coefficient of performance |
| url | https://www.mdpi.com/2076-3417/13/13/7771 |
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