{"title":"用户用电行为分析的加性模型","authors":"Yuzhou Wu, Hanju Li","doi":"10.1109/IAEAC47372.2019.8997882","DOIUrl":null,"url":null,"abstract":"User electricity consumption behavior Analysis is helpful for power supply enterprises to provide personalized services for users, and for the effective implementation of peak load staggering scheme in power supply enterprises. In this paper, the additive model is used to decompose the user electricity consumption, extract the characteristics of electricity trend, periodicity and holiday influence, and construct the modulus 1 vector to describe the user electricity consumption behavior. Then, the power consumption behavior of different users can be analyzed by the unit spherical clustering method, which is convenient for power supply enterprises to provide precision service for different user groups with different characteristics.","PeriodicalId":164163,"journal":{"name":"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Additive Model for User Electricity Consumption Behavior Analysis\",\"authors\":\"Yuzhou Wu, Hanju Li\",\"doi\":\"10.1109/IAEAC47372.2019.8997882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"User electricity consumption behavior Analysis is helpful for power supply enterprises to provide personalized services for users, and for the effective implementation of peak load staggering scheme in power supply enterprises. In this paper, the additive model is used to decompose the user electricity consumption, extract the characteristics of electricity trend, periodicity and holiday influence, and construct the modulus 1 vector to describe the user electricity consumption behavior. Then, the power consumption behavior of different users can be analyzed by the unit spherical clustering method, which is convenient for power supply enterprises to provide precision service for different user groups with different characteristics.\",\"PeriodicalId\":164163,\"journal\":{\"name\":\"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC47372.2019.8997882\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC47372.2019.8997882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Additive Model for User Electricity Consumption Behavior Analysis
User electricity consumption behavior Analysis is helpful for power supply enterprises to provide personalized services for users, and for the effective implementation of peak load staggering scheme in power supply enterprises. In this paper, the additive model is used to decompose the user electricity consumption, extract the characteristics of electricity trend, periodicity and holiday influence, and construct the modulus 1 vector to describe the user electricity consumption behavior. Then, the power consumption behavior of different users can be analyzed by the unit spherical clustering method, which is convenient for power supply enterprises to provide precision service for different user groups with different characteristics.
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