Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System
In order to effectively predict the performance of ground source heat pump system, a performance prediction method is proposed in this paper. Based on the basic model of forward neural network, the algorithm predicts the performance data of ground source heat pump system by inputting the time series...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8807178/ |
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| author | Zhaoyi Zhuang Chen Wei Bing Li Peng Xu Yifei Guo Jiachang Ren |
| author_facet | Zhaoyi Zhuang Chen Wei Bing Li Peng Xu Yifei Guo Jiachang Ren |
| author_sort | Zhaoyi Zhuang |
| collection | DOAJ |
| description | In order to effectively predict the performance of ground source heat pump system, a performance prediction method is proposed in this paper. Based on the basic model of forward neural network, the algorithm predicts the performance data of ground source heat pump system by inputting the time series of system performance and 12 variables including 7 drilling parameters, 2 u-pipe parameters, 2 ground parameters and 1 circulating liquid parameter. The training of the model is divided into three subtasks by the strategy of multi-task learning and co-evolution, where CMA-ES is used as the evolutionary algorithm of the subtask. The experimental results show that the RMSE of the predicted results obtained by the proposed algorithm is less than 0.2, which verifies the effectiveness of the method. At the same time, this algorithm fully considers various influencing factors and has good versatility, which can be used as a reference for the design of ground source heat pump system. |
| format | Article |
| id | doaj-art-6a46d8d46c3b424ab2b344567cde73fc |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6a46d8d46c3b424ab2b344567cde73fc2025-08-20T02:49:09ZengIEEEIEEE Access2169-35362019-01-01711792511793310.1109/ACCESS.2019.29365088807178Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump SystemZhaoyi Zhuang0https://orcid.org/0000-0002-7807-0207Chen Wei1Bing Li2Peng Xu3Yifei Guo4Jiachang Ren5School of Thermal Engineering, Shandong Jianzhu University, Jinan, ChinaShenzhen Graduate School, Peking University, Shenzhen, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaIn order to effectively predict the performance of ground source heat pump system, a performance prediction method is proposed in this paper. Based on the basic model of forward neural network, the algorithm predicts the performance data of ground source heat pump system by inputting the time series of system performance and 12 variables including 7 drilling parameters, 2 u-pipe parameters, 2 ground parameters and 1 circulating liquid parameter. The training of the model is divided into three subtasks by the strategy of multi-task learning and co-evolution, where CMA-ES is used as the evolutionary algorithm of the subtask. The experimental results show that the RMSE of the predicted results obtained by the proposed algorithm is less than 0.2, which verifies the effectiveness of the method. At the same time, this algorithm fully considers various influencing factors and has good versatility, which can be used as a reference for the design of ground source heat pump system.https://ieeexplore.ieee.org/document/8807178/Ground source heat pump systemdata miningcovariance matrix adaptation evolution strategymulti-task learningprediction model |
| spellingShingle | Zhaoyi Zhuang Chen Wei Bing Li Peng Xu Yifei Guo Jiachang Ren Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System IEEE Access Ground source heat pump system data mining covariance matrix adaptation evolution strategy multi-task learning prediction model |
| title | Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System |
| title_full | Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System |
| title_fullStr | Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System |
| title_full_unstemmed | Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System |
| title_short | Performance Prediction Model Based on Multi-Task Learning and Co-Evolutionary Strategy for Ground Source Heat Pump System |
| title_sort | performance prediction model based on multi task learning and co evolutionary strategy for ground source heat pump system |
| topic | Ground source heat pump system data mining covariance matrix adaptation evolution strategy multi-task learning prediction model |
| url | https://ieeexplore.ieee.org/document/8807178/ |
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