Luca Semeraro, E. Crisostomi, A. Franco, A. Landi, Marco Raugi, M. Tucci, G. Giunta
{"title":"电力负荷集群:意大利案例","authors":"Luca Semeraro, E. Crisostomi, A. Franco, A. Landi, Marco Raugi, M. Tucci, G. Giunta","doi":"10.1109/ISGTEUROPE.2014.7028919","DOIUrl":null,"url":null,"abstract":"In this paper we use clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons. This result can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Thus, we compare some conventional features to identify the most informative ones in the Italian case.","PeriodicalId":299515,"journal":{"name":"IEEE PES Innovative Smart Grid Technologies, Europe","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Electrical load clustering: The Italian case\",\"authors\":\"Luca Semeraro, E. Crisostomi, A. Franco, A. Landi, Marco Raugi, M. Tucci, G. Giunta\",\"doi\":\"10.1109/ISGTEUROPE.2014.7028919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we use clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons. This result can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Thus, we compare some conventional features to identify the most informative ones in the Italian case.\",\"PeriodicalId\":299515,\"journal\":{\"name\":\"IEEE PES Innovative Smart Grid Technologies, Europe\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE PES Innovative Smart Grid Technologies, Europe\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISGTEUROPE.2014.7028919\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE PES Innovative Smart Grid Technologies, Europe","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISGTEUROPE.2014.7028919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we use clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons. This result can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Thus, we compare some conventional features to identify the most informative ones in the Italian case.
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