{"title":"CLUSTER SAMPLING FOR THE DEMAND SIDE MANAGEMENT OF POWER BIG DATA","authors":"Yongxin Zhang, Shi Shen","doi":"10.17781/P002208","DOIUrl":null,"url":null,"abstract":"In view of the DSM (Demand Side Management) under the big data environment, an improved FCM (Fuzzy C-Mean) clustering with Gauss data preprocessing is proposed, and the daily load curve of the whole study area was obtained with the electricity data. According to the formulation of the TOU (Time of Use) price, which is consistent with the characteristics of local users is given. The electricity suggestions based on the specific user load curve is provided, including the return of the DR (Demand Response). Subsequently, the sampling division is put forward to expand the improved model. Finally, the method is tested by the actual data, and the results show that it has a processing speed 10 times of the direct processing when the data is more","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of new computer architectures and their applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17781/P002208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In view of the DSM (Demand Side Management) under the big data environment, an improved FCM (Fuzzy C-Mean) clustering with Gauss data preprocessing is proposed, and the daily load curve of the whole study area was obtained with the electricity data. According to the formulation of the TOU (Time of Use) price, which is consistent with the characteristics of local users is given. The electricity suggestions based on the specific user load curve is provided, including the return of the DR (Demand Response). Subsequently, the sampling division is put forward to expand the improved model. Finally, the method is tested by the actual data, and the results show that it has a processing speed 10 times of the direct processing when the data is more
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