Forcasting research of tailored residential demand response based on self⁃learning optimization model

卢 婕; 刘向向; 赵振佐; 赵文辉; 邓娜娜; 王 博

doi:10.3969/j.issn.1009-1831.2021.06.017

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Forcasting research of tailored residential demand response based on self⁃learning optimization model

更新时间：2026-03-05

- Forcasting research of tailored residential demand response based on self⁃learning optimization model
- Vol. 23, Issue 6, (2021)
- 作者机构：
  
  1. 国网江西省电力有限公司供电服务管理中心,南昌,330001
  2. 北京理工大学管理与经济学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2021.06.017
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
卢婕, 刘向向, 赵振佐, et al. Forcasting research of tailored residential demand response based on self⁃learning optimization model[J]. 2021, 23(6).
DOI：

卢婕, 刘向向, 赵振佐, et al. Forcasting research of tailored residential demand response based on self⁃learning optimization model[J]. 2021, 23(6). DOI： 10.3969/j.issn.1009-1831.2021.06.017.

摘要

基于家庭智能电能表采集数据，构建居民需求响应样本库。基于该样本库，开展特征工程，充分挖掘响应用户的家庭属性、响应行为、用电行为等特征。在此基础上，构建居民电力需求响应神经网络自学习优化模型，根据不同家庭标签与历史响应结果数据，预测居民需求响应参与情况，随着典型场景下需求响应的不断开展，对模型进行循环迭代与优化。最终，依据调节目标，智能化制定需求响应调控策略。算例结果表明，所提的需求响应策略能够准确识别居民需求响应参与度，降低需求响应激励成本。

Abstract

Based on the monitoring data of household smart meters

a sample database of residents’demand response is constructed. Based on the sample database

feature engineering is carried out to fully mine the characteristics of responsive users

such as family attributes

response behavior and power consumption behavior. On this basis

the self-learning optimization model of resident power demand response neural network is constructed. According to the data of different family labels and historical response results

the participation of resident demand response is predicted. With the continuous development of demand response in typical scenarios

the model is iterated and optimized. Finally

according to the regulation objectives

the demand response regulation strategy is intelligently formulated. Results show that the proposed demand response strategy can accurately identify the residential demand response participation and reduce the demand response incentive cost.

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