广东电力2026，Vol.39Issue(8)：75-89,15.DOI:10.3969/j.issn.1007-290X.2026.08.008
基于层次化特征解耦迁移的增量用户负荷精细画像方法
Incremental User Load Fine-grained Profiling Method Based on Hierarchical Feature Decoupling and Transfer
摘要
Abstract
To address the performance degradation in incremental user load profiling caused by data scarcity,an incremental user load fine-grained profiling method based on hierarchical feature decoupling and transfer is proposed.This method treats data-abundant existing users as the source domain and provides profiling support for incremental users through hierarchical feature disentanglement and cross-domain alignment.First,a shared encoder integrating multi-scale convolution,Transformer and bidirectional long short-term memory(BiLSTM)networks is pre-trained to extract multi-scale temporal features from load sequences.Second,a hierarchical feature disentanglement module decomposes temporal features into domain-invariant,cluster-shared and domain-specific components at the global,group and individual levels.The decomposed features characterize common consumption patterns,group-level load modes and individual consumption behaviors respectively.Then,a dynamic clustering mechanism is introduced to match incremental users with similar existing user groups,and cross-domain feature alignment is achieved by combining contrastive learning and adversarial training.Finally,the encoder parameters are frozen and the profiling layer is fine-tuned using a small amount of target domain data.Experimental results on real load data from a city in southern China show that the proposed method outperforms conventional transfer learning methods in incremental user load forecasting,demonstrating its effectiveness for load pattern modeling and transfer adaptation.关键词
负荷画像/负荷预测/迁移学习/域适应/层次化特征解耦/动态聚类Key words
load profiling/load forecasting/transfer learning/domain adaptation/hierarchical feature decoupling/dynamic clustering分类
信息技术与安全科学引用本文复制引用
许志恒,杨梓晴,陈沛东,余涛,胡小磊,张桦晖..基于层次化特征解耦迁移的增量用户负荷精细画像方法[J].广东电力,2026,39(8):75-89,15.基金项目
中国南方电网有限责任公司科技项目[037700KC23120014(GDKJXM20231346)] （GDKJXM20231346）
