{"title":"基于层次聚类和上尺度方法的区域风电预测","authors":"Ke Wang, Yao Zhang, Fan Lin, Yang Xu","doi":"10.1109/AEEES51875.2021.9403004","DOIUrl":null,"url":null,"abstract":"For a long time, the world has been committed to optimizing the energy structure to reduce carbon emissions, so that renewable energies, for example wind power, have been widely integrated into power system. A large number of random and fluctuating wind power makes the system bear more and more risks, which has caused the dispatching department to pay increasing attention to regional wind power output. Although the upscaling method is widely used to predict regional wind power output, it still has shortcomings. This paper proposes a regional wind power prediction based on hierarchical clustering and upscaling method. This approach uses a greedy algorithm to search for the optimal number of sub-regions. Finally, the effectiveness of the proposed forecasting approach has been verified on real-world data.","PeriodicalId":356667,"journal":{"name":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Regional Wind Power Forecasting Based on Hierarchical Clustering and Upscaling Method\",\"authors\":\"Ke Wang, Yao Zhang, Fan Lin, Yang Xu\",\"doi\":\"10.1109/AEEES51875.2021.9403004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For a long time, the world has been committed to optimizing the energy structure to reduce carbon emissions, so that renewable energies, for example wind power, have been widely integrated into power system. A large number of random and fluctuating wind power makes the system bear more and more risks, which has caused the dispatching department to pay increasing attention to regional wind power output. Although the upscaling method is widely used to predict regional wind power output, it still has shortcomings. This paper proposes a regional wind power prediction based on hierarchical clustering and upscaling method. This approach uses a greedy algorithm to search for the optimal number of sub-regions. Finally, the effectiveness of the proposed forecasting approach has been verified on real-world data.\",\"PeriodicalId\":356667,\"journal\":{\"name\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEEES51875.2021.9403004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEEES51875.2021.9403004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Regional Wind Power Forecasting Based on Hierarchical Clustering and Upscaling Method
For a long time, the world has been committed to optimizing the energy structure to reduce carbon emissions, so that renewable energies, for example wind power, have been widely integrated into power system. A large number of random and fluctuating wind power makes the system bear more and more risks, which has caused the dispatching department to pay increasing attention to regional wind power output. Although the upscaling method is widely used to predict regional wind power output, it still has shortcomings. This paper proposes a regional wind power prediction based on hierarchical clustering and upscaling method. This approach uses a greedy algorithm to search for the optimal number of sub-regions. Finally, the effectiveness of the proposed forecasting approach has been verified on real-world data.