电力需求侧管理2024，Vol.26Issue(1)：16-22,7.DOI:10.3969/j.issn.1009-1831.2024.01.003
负荷数据特征分析的用户集群需求响应潜力预测方法
Demand response potential prediction method with load data features analysis of user clusters
摘要
Abstract
With the gradual advancement of electricity market reform,demand response will play an increasingly important role in future new power systems.Considering the problems of cumbersome process of DR potential calculation and lack of detailed data on user electric-ity consumption process,a group users DR potential prediction and classification method based on historical load,temperature,and elec-tricity price data is proposed.First,data processing and information extraction are carried out on the user's daily electricity load curves.Three characteristics,monthly load regularity,daily load fluctuation and peak-valley consistency,are calculated,which form an index sys-tem of physical regulative potential evaluation.Further,nonlinear au-to-regressive model with exogenous inputs neural network is applied to the prediction of group users'daily load and DR potential.Finally,taking industrial users as examples,Meanshift algorithm is used to partition user clusters,and DR regulation power of general component manufacturing industry is predicted.By comparing and analyzing with actual data,the effectiveness of the proposed method in this paper is verified.关键词
负荷数据/需求响应潜力/负荷特征/用户集群/非线性自回归神经网络Key words
load data/demand response potential/load characteristics/user clusters/nonlinear au-to-regressive model with exogenous in-puts neural network分类
信息技术与安全科学引用本文复制引用
黄奇峰,杨世海,段梅梅,孔月萍,丁泽诚..负荷数据特征分析的用户集群需求响应潜力预测方法[J].电力需求侧管理,2024,26(1):16-22,7.基金项目
国网江苏省电力有限公司科技项目(J2022127) （J2022127）
