电器与能效管理技术Issue(6)：22-31,10.DOI:10.16628/j.cnki.2095-8188.2025.06.004
基于对抗性判别域自适应的未知新增设备非侵入式负荷识别方法
Non-Intrusive Load Identification Method for Unknown Added Devices Based on Adversarial Discriminative Domain Adaptation
摘要
Abstract
Aiming at the problem that the differences in signal characteristics between different electrical devices lead to the decrease in accuracy and insufficient generalization ability of traditional non-intrusive load recognition methods when facing unknown additional devices,a migration learning method based on the adversarial Discriminative domain adaptation(ADDA)is proposed.First,the multi-dimensional feature parameters of different devices are extracted by combining feature selection methods,and the source domain models are trained using the source device feature data.Then,an adversarial training strategy is employed,with a discriminator introduced to optimize the feature extractor of the target device.Finally,the model is fine-tuned and optimized to complete the load recognition task on the target device.Experimental results show that the optimized recognition model achieves an average accuracy of 98.90％,an improvement of 18.41％compared to the traditional migration learning methods.关键词
非侵入式负荷监测/未知新增设备/对抗性判别域自适应/迁移学习Key words
non-intrusive load monitoring/unknown added devices/adversarial discriminative domain adaptation(ADDA)/transfer learning分类
信息技术与安全科学引用本文复制引用
何胜,杨皓文,赵婧冰..基于对抗性判别域自适应的未知新增设备非侵入式负荷识别方法[J].电器与能效管理技术,2025,(6):22-31,10.基金项目
国家重点研发计划项目(2023YFC3807000) （2023YFC3807000）
