{"title":"熵在典型负荷曲线分类中的应用","authors":"D. Hock, Martin Kappes","doi":"10.1109/ICSGCE.2018.8556687","DOIUrl":null,"url":null,"abstract":"Clusters of energy consumers can be utilized to improve the accuracy of load forecasting or to introduce differentiated and personalized tariffs according to the consumers energy demand. In this paper, we aim to classify energy load curves according to their similarity with other households. We present the entropy as quantitative metric for Typical Load Curve Classification and clustering. Furthermore, we present the likelihood of time periods to uniquely distinguish load curves of residential households and approximate the minimal required time resolution for classification tasks. Results, using a real world data set, we confirm the practical relevance and feasibility of our approach.","PeriodicalId":366392,"journal":{"name":"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)","volume":"10 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using the Entropy for Typical Load Curve Classification\",\"authors\":\"D. Hock, Martin Kappes\",\"doi\":\"10.1109/ICSGCE.2018.8556687\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clusters of energy consumers can be utilized to improve the accuracy of load forecasting or to introduce differentiated and personalized tariffs according to the consumers energy demand. In this paper, we aim to classify energy load curves according to their similarity with other households. We present the entropy as quantitative metric for Typical Load Curve Classification and clustering. Furthermore, we present the likelihood of time periods to uniquely distinguish load curves of residential households and approximate the minimal required time resolution for classification tasks. Results, using a real world data set, we confirm the practical relevance and feasibility of our approach.\",\"PeriodicalId\":366392,\"journal\":{\"name\":\"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)\",\"volume\":\"10 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSGCE.2018.8556687\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSGCE.2018.8556687","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using the Entropy for Typical Load Curve Classification
Clusters of energy consumers can be utilized to improve the accuracy of load forecasting or to introduce differentiated and personalized tariffs according to the consumers energy demand. In this paper, we aim to classify energy load curves according to their similarity with other households. We present the entropy as quantitative metric for Typical Load Curve Classification and clustering. Furthermore, we present the likelihood of time periods to uniquely distinguish load curves of residential households and approximate the minimal required time resolution for classification tasks. Results, using a real world data set, we confirm the practical relevance and feasibility of our approach.