Demand response potential prediction method with load data features analysis of user clusters

黄奇峰; 杨世海; 段梅梅; 孔月萍; 丁泽诚

doi:10.3969/j.issn.1009-1831.2024.01.003

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Demand response potential prediction method with load data features analysis of user clusters

更新时间：2026-03-05

- Demand response potential prediction method with load data features analysis of user clusters
- Vol. 26, Issue 1, (2024)
- 作者机构：
  
  国网江苏省电力有限公司营销服务中心,南京,210020
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2024.01.003
  CLC：
- Published：2024
- 稿件说明：
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黄奇峰, 杨世海, 段梅梅, et al. Demand response potential prediction method with load data features analysis of user clusters[J]. 2024, 26(1).
DOI：

黄奇峰, 杨世海, 段梅梅, et al. Demand response potential prediction method with load data features analysis of user clusters[J]. 2024, 26(1). DOI： 10.3969/j.issn.1009-1831.2024.01.003.

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。

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 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.

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