电力需求侧管理2025，Vol.27Issue(3)：18-24,7.DOI:10.3969/j.issn.1009-1831.2025.03.003
一种基于云边协同的非侵入式负荷辨识框架
Non-intrusive load identification framework based on cloud-edge collaboration
摘要
Abstract
In order to solve the problem of analysis ductility and large consumption of cloud resources caused by massive non-intrusive load monitoring(NILM)data uploaded to the cloud,a non-intrusive load identification framework based on cloud-edge collaboration is proposed.Firstly,the Markov transition field(MTF)coding method is used to color code the power data,and the load identification with clear characteristics is constructed.Then,a lightweight deep learning model with the same structure is deployed in the cloud service layer and the edge service layer respectively to complete the training and load identification tasks.While reducing the pressure of cloud-edge re-sources,the cloud-edge coordination of load identification is realized through transfer learning.Finally,based on adaptive synthetic sam-pling(ADASYN),the REDD dataset is extended to solve the model learning bias caused by dataset imbalance,and the identification per-formance of the framework proposed is validated based on the dataset.The results show that the framework can not only meet the require-ments of high precision and real-time load identification,but also significantly reduce the pressure of cloud and edge storage and comput-ing resources.关键词
非侵入式负荷辨识/云边协同/马尔可夫转移场/轻量级深度学习模型/自适应合成采样Key words
non-intrusive load identification/cloud-edge collaboration/Markov transition field/lightweight deep learning model/adaptive synthetic sampling分类
信息技术与安全科学引用本文复制引用
顾水福,周磊,李洁,李亚飞,李圆琪,朱超群..一种基于云边协同的非侵入式负荷辨识框架[J].电力需求侧管理,2025,27(3):18-24,7.基金项目
国网江苏省电力有限公司科技项目(J2022093) （J2022093）
