摘要
负荷预测是电力系统安全经济运行的前提。随着电力系统的市场化和能源互联网的研究与发展,高质量的负荷预测显得愈发重要。分析了影响负荷预测的因素,对数据进行收集及挖掘,采用了基于支持向量机负荷预测算法对区域负荷进行短期预测,并进一步开展了针对城区的精细化负荷预测研究。结合某地区案例,对该算法进行验证,结果表明,该算法预测结果优越,相对误差率较小。
Load forecasting is a prerequisite for safe and economical operation of power system. With the market reform of power system and research and development of the energy Internet,high-quality load forecast becomes increasingly im-portant. This paper analyzes the factors that influence the load forecasting,collects and excavates the data,uses the support vector machine load forecasting algorithm to forecast the regi-onal short-term load,and further develops the fine load fore-casting research for the urban area. According to a test of this forecasting algorithm for a case in an urban area,the result shows that the better the forecasting result,the lower the relative rate.
作者
万强
王清亮
王睿豪
黄朝晖
白云飞
陈大军
栗维勋
WAN Qiang WANG Qingliang WANG Ruihao HUANG Zhaohui BAI Yunfei CHEN Dajun LI Weixun(State Grid Shijiazhuang Power Supply Company, Shijiazhuang 050000, Hebei, China School of Electrical Engineering, Xi＇an Jiaotong University, Xi＇an 710049, Shaanxi, China)
出处
《电网与清洁能源》
北大核心
2016年第12期14-20,共7页
Power System and Clean Energy
基金
国家自然科学基金资助项目(51607136)~~
关键词
短期负荷预测
数据发掘
支持向量机
short-term load forecasting
data mining
support vector machine
