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

中文关键词: 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

英文关键词: load data  demand response potential  load characteristics  user clusters  nonlinear au-to-regressive model with exogenous in-puts neural network

中文摘要:

      随着电力市场改革的逐步推进，需求响应将在未来新型电力系统中发挥越来越重要作用。针对目前DR潜力计算过程繁琐、用户数据不足的问题，提出了一种基于用户历史负荷、气温和电价数据的用户集群DR潜力预测方法。首先，通过对用户的历史负荷曲线进行数据处理和信息提取，从月负荷规律性、日负荷波动性、峰谷一致性3个方面对各用户的用电行为进行特征值计算，形成评估用户类型的指标体系。继而，提出基于时序带有外部输入的非线性自回归神经网络的用户负荷和DR潜力预测方法。最后，以工业用户为例采用Meanshift算法实现用户集群划分，并对通用零部件制造行业的DR调节功率进行预测，经与实际调节功率数据进行对比分析，验证了本文所提方法的有效性。

英文摘要:

      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 electricity consumption process, a group users DR potential prediction and classification method based on historical load, temperature, and electricity 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 system 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.