{"title":"分段回归分析:设计更有效的需求侧管理方案的方法","authors":"Imran Khan","doi":"10.1109/SUSTECH.2018.8671364","DOIUrl":null,"url":null,"abstract":"A new analytical approach, time-segmented regression analysis (TSRA) has been introduced for the first time. TSRA is capable of identifying the dominating household factors responsible for peak time electricity consumption at residences. Therefore, identification of these household factors along with their time-varying nature would enable the policymakers to design more effective demand-side management (DSM) strategies to reduce peak-time electricity demand at residences.","PeriodicalId":127111,"journal":{"name":"2018 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Time-segmented Regression Analysis: An Approach in Designing more Effective DSM Scheme\",\"authors\":\"Imran Khan\",\"doi\":\"10.1109/SUSTECH.2018.8671364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new analytical approach, time-segmented regression analysis (TSRA) has been introduced for the first time. TSRA is capable of identifying the dominating household factors responsible for peak time electricity consumption at residences. Therefore, identification of these household factors along with their time-varying nature would enable the policymakers to design more effective demand-side management (DSM) strategies to reduce peak-time electricity demand at residences.\",\"PeriodicalId\":127111,\"journal\":{\"name\":\"2018 IEEE Conference on Technologies for Sustainability (SusTech)\",\"volume\":\"102 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Conference on Technologies for Sustainability (SusTech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SUSTECH.2018.8671364\",\"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 IEEE Conference on Technologies for Sustainability (SusTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SUSTECH.2018.8671364","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Time-segmented Regression Analysis: An Approach in Designing more Effective DSM Scheme
A new analytical approach, time-segmented regression analysis (TSRA) has been introduced for the first time. TSRA is capable of identifying the dominating household factors responsible for peak time electricity consumption at residences. Therefore, identification of these household factors along with their time-varying nature would enable the policymakers to design more effective demand-side management (DSM) strategies to reduce peak-time electricity demand at residences.
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