A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction
Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001083 |
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| author | Weipeng Li Yuting Chong Xin Guo Jun Liu |
| author_facet | Weipeng Li Yuting Chong Xin Guo Jun Liu |
| author_sort | Weipeng Li |
| collection | DOAJ |
| description | Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons. |
| format | Article |
| id | doaj-art-74e3e48e2ff6466086359fc7a883546e |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-74e3e48e2ff6466086359fc7a883546e2024-12-18T08:53:06ZengElsevierEnergy and AI2666-54682024-12-0118100442A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extractionWeipeng Li0Yuting Chong1Xin Guo2Jun Liu3School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, ChinaSchool of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, ChinaSchool of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Corresponding author at: School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China.School of Automation and Information Engineering, Xi’an University of Technology, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, China; Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, No. 5 Jinhua South Road, Xi’an, 710048, Shaanxi, ChinaEfficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.http://www.sciencedirect.com/science/article/pii/S2666546824001083Wind power predictionData-drivenHybrid modelSeasonal feature decompositionGated recurrent networksAttention mechanism |
| spellingShingle | Weipeng Li Yuting Chong Xin Guo Jun Liu A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction Energy and AI Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism |
| title | A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| title_full | A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| title_fullStr | A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| title_full_unstemmed | A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| title_short | A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| title_sort | hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction |
| topic | Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001083 |
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