Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction
Abstract Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12956 |
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| _version_ | 1841546542429765632 |
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| author | Yalong Li Ye Jin Yangqing Dan Wenting Zha |
| author_facet | Yalong Li Ye Jin Yangqing Dan Wenting Zha |
| author_sort | Yalong Li |
| collection | DOAJ |
| description | Abstract Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model. |
| format | Article |
| id | doaj-art-e997f67410534aacb1a77c5c2b983eb7 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-e997f67410534aacb1a77c5c2b983eb72025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142195220810.1049/rpg2.12956Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correctionYalong Li0Ye Jin1Yangqing Dan2Wenting Zha3School of Mechanical and Electrical Engineering China University of Mining and Technology‐Beijing Beijing ChinaSchool of Mechanical and Electrical Engineering China University of Mining and Technology‐Beijing Beijing ChinaEconomic Research Institute, State Grid Zhejiang Electric Power Company Zhejiang ChinaSchool of Mechanical and Electrical Engineering China University of Mining and Technology‐Beijing Beijing ChinaAbstract Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model.https://doi.org/10.1049/rpg2.12956error analysisfeature extractionneural net architecturewind farm design and operationwind power |
| spellingShingle | Yalong Li Ye Jin Yangqing Dan Wenting Zha Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction IET Renewable Power Generation error analysis feature extraction neural net architecture wind farm design and operation wind power |
| title | Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction |
| title_full | Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction |
| title_fullStr | Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction |
| title_full_unstemmed | Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction |
| title_short | Hybrid model for wind power estimation based on BIGRU network and error discrimination‐correction |
| title_sort | hybrid model for wind power estimation based on bigru network and error discrimination correction |
| topic | error analysis feature extraction neural net architecture wind farm design and operation wind power |
| url | https://doi.org/10.1049/rpg2.12956 |
| work_keys_str_mv | AT yalongli hybridmodelforwindpowerestimationbasedonbigrunetworkanderrordiscriminationcorrection AT yejin hybridmodelforwindpowerestimationbasedonbigrunetworkanderrordiscriminationcorrection AT yangqingdan hybridmodelforwindpowerestimationbasedonbigrunetworkanderrordiscriminationcorrection AT wentingzha hybridmodelforwindpowerestimationbasedonbigrunetworkanderrordiscriminationcorrection |