一种电力用户用电特征数据挖掘方法  

Power Consumption Characteristic Data Mining Method for Power User

作　　者：李雄 吴方权 汤成佳 Li Xiong;Wu Fangquan;Tang Chengjia(Information Center of Guizhou Power Grid Company Limited,Guiyang 550003,China)

机构地区：[1]贵州电网有限责任公司信息中心,贵阳550003

出　　处：《兵工自动化》2025年第3期25-28,共4页Ordnance Industry Automation

摘　　要：针对目前电力需求响应分析中存在缺乏对不同环境和激励信号下用户响应行为的分析和预测的问题,提出一种基于电力用户用电行为数据挖掘方法。通过构建并分析基于激励的需求响应体系结构,且基于现有的用户响应成本抽象公式,建立用户响应灵活度模型;同时提出一种双层长短时记忆网络识别用户响应行为模型,将所提模型与随机森林(random forest,RF)、支持向量机(support vector machines,SVM)、递归神经网络(recurrent neural network,RNN)、长短时记忆(long short-term memory,LSTM)等模型进行对比。结果表明:所提模型性能优异,准确率为94.83%,F1分数为95.45%,品质因数为39.42%,可对电力安全运行管理的发展提供一定借鉴。In order to solve the problem of lack of analysis and prediction of user's response behavior under different environments and incentive signals in the current power demand response analysis,a data mining method based on power user's power consumption behavior is proposed.Construct and analyze the incentive-based demand response architecture,establish a user response flexibility model based on the existing abstract formula of user response cost,and propose a double-layer longshort-term memory network to identify user response behavior model;The proposed model is compared with random forest(RF),support vector machines(SVM),recurrent neural network(RNN)and long short-term memory(LSTM).The results show that the proposed model has excellent performance,the accuracy rate is 94.83%,the F1 score is 95.45%,and the quality factor is 39.42%,which can provide a reference for the development of safe operation and management of electric power.

关 键 词：电力系统 需求响应 深度学习 长短时记忆 行为识别 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]