{"title":"基于PatchTST和时间卷积网络的风电功率预测模型","authors":"Mingju Gong, Yining Wang, Jiabin Huang, Hanwen Cui, Shaomin Jing, Fan Zhang","doi":"10.1002/ep.14584","DOIUrl":null,"url":null,"abstract":"<p>Due to the unpredictable nature of wind, wind power forecasting still faces certain challenges. The accuracy of wind power prediction plays a crucial role in the stability of the whole system. To improve the accuracy of wind power prediction, this research proposed an innovative hybrid prediction model that utilizes a multi-layer perceptron, combined with a temporal convolutional network and PatchTST. Firstly, a multi-layer perceptron is introduced to capture higher-order features, and a temporal convolutional network is used to extract time-domain features from the dataset to capture the dynamic changes of wind speed; then, PatchTST is used to accurately forecast wind power. The results show that the proposed model performs well in terms of prediction accuracy and prediction speed. The minimal MAPE is 14.4%, the prediction accuracy is improved by 9.22%, and the power generation efficiency is increased by 0.31%. In addition, this research used Bootstrapping to estimate the probability interval of wind power to provide a more comprehensive wind power forecast. This study provides a new and effective tool in the field of wind power forecasting, helping to improve the stability of power systems.</p>","PeriodicalId":11701,"journal":{"name":"Environmental Progress & Sustainable Energy","volume":"44 3","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel wind power prediction model based on PatchTST and temporal convolutional network\",\"authors\":\"Mingju Gong, Yining Wang, Jiabin Huang, Hanwen Cui, Shaomin Jing, Fan Zhang\",\"doi\":\"10.1002/ep.14584\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Due to the unpredictable nature of wind, wind power forecasting still faces certain challenges. The accuracy of wind power prediction plays a crucial role in the stability of the whole system. To improve the accuracy of wind power prediction, this research proposed an innovative hybrid prediction model that utilizes a multi-layer perceptron, combined with a temporal convolutional network and PatchTST. Firstly, a multi-layer perceptron is introduced to capture higher-order features, and a temporal convolutional network is used to extract time-domain features from the dataset to capture the dynamic changes of wind speed; then, PatchTST is used to accurately forecast wind power. The results show that the proposed model performs well in terms of prediction accuracy and prediction speed. The minimal MAPE is 14.4%, the prediction accuracy is improved by 9.22%, and the power generation efficiency is increased by 0.31%. In addition, this research used Bootstrapping to estimate the probability interval of wind power to provide a more comprehensive wind power forecast. This study provides a new and effective tool in the field of wind power forecasting, helping to improve the stability of power systems.</p>\",\"PeriodicalId\":11701,\"journal\":{\"name\":\"Environmental Progress & Sustainable Energy\",\"volume\":\"44 3\",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Progress & Sustainable Energy\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ep.14584\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Progress & Sustainable Energy","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ep.14584","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
A novel wind power prediction model based on PatchTST and temporal convolutional network
Due to the unpredictable nature of wind, wind power forecasting still faces certain challenges. The accuracy of wind power prediction plays a crucial role in the stability of the whole system. To improve the accuracy of wind power prediction, this research proposed an innovative hybrid prediction model that utilizes a multi-layer perceptron, combined with a temporal convolutional network and PatchTST. Firstly, a multi-layer perceptron is introduced to capture higher-order features, and a temporal convolutional network is used to extract time-domain features from the dataset to capture the dynamic changes of wind speed; then, PatchTST is used to accurately forecast wind power. The results show that the proposed model performs well in terms of prediction accuracy and prediction speed. The minimal MAPE is 14.4%, the prediction accuracy is improved by 9.22%, and the power generation efficiency is increased by 0.31%. In addition, this research used Bootstrapping to estimate the probability interval of wind power to provide a more comprehensive wind power forecast. This study provides a new and effective tool in the field of wind power forecasting, helping to improve the stability of power systems.
期刊介绍:
Environmental Progress , a quarterly publication of the American Institute of Chemical Engineers, reports on critical issues like remediation and treatment of solid or aqueous wastes, air pollution, sustainability, and sustainable energy. Each issue helps chemical engineers (and those in related fields) stay on top of technological advances in all areas associated with the environment through feature articles, updates, book and software reviews, and editorials.