{"title":"负载数据清理和总线负载符合因子","authors":"Wenyuan Li, Ke Wang, W. Wangdee","doi":"10.1201/B16908-16","DOIUrl":null,"url":null,"abstract":"Load curve data refer to power consumptions recorded by meters at certain time intervals at buses of individual substations. Load curve data are one of the most important datasets collected and retained by utilities. The analysis of load curve data would greatly improve day-to-day operations, system analysis in smart grids, system visualization, system performance reliability, energy saving, and accuracy in system planning [1–4].","PeriodicalId":36842,"journal":{"name":"Technology and Economics of Smart Grids and Sustainable Energy","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Load Data Cleansing and Bus Load Coincidence Factors\",\"authors\":\"Wenyuan Li, Ke Wang, W. Wangdee\",\"doi\":\"10.1201/B16908-16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Load curve data refer to power consumptions recorded by meters at certain time intervals at buses of individual substations. Load curve data are one of the most important datasets collected and retained by utilities. The analysis of load curve data would greatly improve day-to-day operations, system analysis in smart grids, system visualization, system performance reliability, energy saving, and accuracy in system planning [1–4].\",\"PeriodicalId\":36842,\"journal\":{\"name\":\"Technology and Economics of Smart Grids and Sustainable Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2017-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technology and Economics of Smart Grids and Sustainable Energy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/B16908-16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology and Economics of Smart Grids and Sustainable Energy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/B16908-16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Load Data Cleansing and Bus Load Coincidence Factors
Load curve data refer to power consumptions recorded by meters at certain time intervals at buses of individual substations. Load curve data are one of the most important datasets collected and retained by utilities. The analysis of load curve data would greatly improve day-to-day operations, system analysis in smart grids, system visualization, system performance reliability, energy saving, and accuracy in system planning [1–4].
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