电力需求侧管理2025，Vol.27Issue(3)：11-17,7.DOI:10.3969/j.issn.1009-1831.2025.03.002
基于时序迁移策略的空调负荷需求响应潜力评估
Potential evaluation of air conditioning load demand response based on time-sequential migration strategy
摘要
Abstract
The accurate assessment of the demand response potential of air conditioning loads is a crucial foundational task for effectively scheduling their participation in demand response.To address the issue of low prediction accuracy caused by the traditional deep learning methods'neglect of the time-sequential distribution differences in real-world air conditioning loads,the concept of transfer learning to the time dimension is extended.The phenomenon of time-sequential distribution drift in air conditioning load time series is analyzed by draw-ing an analogy to covariate shift in transfer learning.Based on this,two time-sequential migration strategies,time-sequential distribution matching and time-sequential similarity quantification,are proposed.These strategies are integrated into the traditional recurrent neural network(RNN)architecture to build an adaptive RNN air conditioning load prediction model,thereby improving the prediction accuracy in real-world scenarios.Finally,an air conditioning load demand response potential evaluation method is proposed based on the overall ap-proach of predicting the load values before and after the response and the adaptive RNN air conditioning load prediction model.Compara-tive experimental analysis on real datasets shows that this method can significantly improve the prediction accuracy of demand response po-tential over existing methods,thus providing effective reference for the demand response scheduling decisions of the grid dispatch center.关键词
空调负荷/需求响应潜力/时序迁移策略/循环神经网络/迁移学习Key words
air conditioning load/demand response potential/time-sequential migration strategy/recurrent neural network/transfer learning分类
动力与电气工程引用本文复制引用
龙禹,王雨薇,任禹丞,郑杨,费伟伟,刘陈城,刘京易..基于时序迁移策略的空调负荷需求响应潜力评估[J].电力需求侧管理,2025,27(3):11-17,7.基金项目
国网江苏省电力有限公司科技项目(J2023176) （J2023176）
