Classification Analysis Method for Electricity Consumption Behavior Based on Extreme Learning Machine Algorithm

被引：0
作者：
Lu J. [1 ]
Chen Z. [1 ]
Gong G. [1 ]
Xu Z. [2 ]
Qi B. [1 ]
机构：
[1] Beijing Engineering Research Center of Energy Electric Power Information Security (North China Electric Power University), Beijing
[2] Economic Technology Institute Design Center, State Grid Hunan Electric Power Company Limited, Changsha
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 02期
关键词：
Back propagation neural network; Big data; Demand response; Electricity consumption behavior analysis; Extreme learning machine(ELM); Intelligent electricity consumption; Parameter optimization;
D O I：
10.7500/AEPS20171214003
中图分类号：
学科分类号：
摘要：
Aiming at the classification problem of electricity consumption analysis of smart users under the background of big data, a classification method based on extreme learning machine (ELM) algorithm is proposed for electricity consumption behavior analysis. Firstly, based on the previous research of feature preference for electricity consumption behavior of smart users, the feature preference strategy is adopted to extract the best feature sets of the load curve, which helps to classify and analyze the data of electricity consumption for users. Then, the best feature sets are used as the input of ELM network. By comparing the accuracy of the training set and the test set with different hidden layer excitation functions and hidden layer node numbers, input parameters of ELM algorithm are selected, which are suitable for user's electricity consumption behavior analysis. Finally, the domestic and foreign electricity consumption data is taken as the data source to carry out the simulation experiment. Through the comparison and analysis with back propagation (BP) neural network, the results show that the analysis of electricity consumption behavior based on ELM algorithm improves the detection accuracy and reduces the algorithm operation time, which can better grasp the user load status and realize load balance of distribution network. © 2019 Automation of Electric Power Systems Press.
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页码：97 / 104
页数：7
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