摘要
机器学习通过学习已有大量的数据形成预测及判断,能够挖掘数据内在价值,在用电数据分析领域取得丰富成果。基于对机器学习算法应用于用电数据分析的有效分析,给出应用机器学习算法进行用电数据分析的整体分析框架,重点分析应用机器学习算法在非侵入式负荷分解、负荷曲线聚类以及负荷预测3个应用的研究现状,并且针对每种应用给出了研究建议。
Machine learning can mine the intrinsic value of data by learning a large number of data to form prediction and judgment,and has achieved rich results in the field of power data analysis.Based on the effective analysis of machine learning algorithm applied to power data analysis,this paper presents the overall analysis framework of machine learning algorithm for power data analysis,focusing on the application of machine learning algorithm in three applications of non-invasive load decomposition,load curve clustering and load forecasting,and gives research suggestions for each application.
作者
李特
张家驹
赵炜
侯泽鹏
LI Te;ZHANG Jiaju;ZHAO Wei;HOU Zepeng(State Grid Hebei Electric Power Co.,Ltd.Information and Communication Branch,Shijiazhuang 050021,China)
出处
《河北电力技术》
2020年第5期17-21,共5页
Hebei Electric Power
关键词
机器学习
用电数据
负荷聚类
负荷预测
非侵入式负荷分解
machine learning
power consumption data
load clustering
load forecasting
noninvasive load decomposition