Short-Term Load Forecasting Based on Big Data Technologies

查看全文 作　　者：Pei [1]Zhang;Xiaoyu [2]Wu;Xiaojun [2]Wang;Sheng [2]Bi

机构地区：[1]NARI Accenture Information Technology Center,Beijing 100044,China;[2]School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China

出　　处：《CSEE Journal of Power and Energy Systems》索引2015年第1卷第3期,共9页

摘　　要：With the construction of smart grid,lots of renewable energy resources such as wind and solar are deployed in power system.It might make the power system load varied complex than before which will bring difficulties in short-term load forecasting area.To overcome this issue,this paper proposes a new short-term load forecasting framework based on big data technologies.First,a cluster analysis is performed to classify daily load patterns for individual loads using smart meter data.Next,an association analysis is used to determine critical influential factors.This is followed by the application of a decision tree to establish classification rules.Then,appropriate forecasting models are chosen for different load patterns.Finally,the forecasted total system load is obtained through an aggregation of an individual load’s forecasting results.Case studies using real load data show that the proposed new framework can guarantee the accuracy of short-term load forecasting within required limits. 关键词：Association analysis big data cluster analysis decision tree short-term load forecasting

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Short-Term Load Forecasting Based on Big Data Technologies

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