Lan Chaofan, Luo Qingquan, Yu Tao, Liang Minhang, Pan Zhenning
The School of Electrical Power Engineering, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel). 2025 Jun 11;25(12):3667. doi: 10.3390/s25123667.
Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the increasing diversification of electrical appliances, real-time NILM systems require continuous integration of knowledge from new client-side appliance data to maintain monitoring effectiveness. However, current methods face challenges with inter-client knowledge conflicts and catastrophic forgetting in distributed multi-client continual learning scenarios. This study addresses these challenges by proposing a confidence-based collaborative distributed continual learning framework for NILM. A lightweight layer-wise dual-supervised autoencoder (LWDSAE) model is initially designed for smart meter deployment, supporting both load identification and confidence-based collaboration tasks. Clients with learning capabilities generate new models through one-time fine-tuning, facilitating collaboration among client models and enhancing individual client load identification performance via a confidence judgment method based on signal reconstruction deviations. Furthermore, an anomaly sample detection-driven model portfolios update method is developed to assist each client in maintaining optimal local performance under model quantity constraints. Comprehensive evaluations on two public datasets and real-world applications demonstrate that the framework achieves sustained performance improvements in distributed continual learning scenarios, consistently outperforming state-of-the-art methods.
非侵入式负载监测（NILM）是一种通过分析聚合电测量数据来提取电器级能耗信息的技术，已成为智能电网和能源管理应用的关键技术。鉴于电器种类日益多样化，实时NILM系统需要不断整合来自新客户端电器数据的知识，以保持监测效果。然而，在分布式多客户端持续学习场景中，当前方法面临客户端间知识冲突和灾难性遗忘的挑战。本研究通过提出一种基于置信度的协作式分布式持续学习框架来解决这些挑战。首先为智能电表部署设计了一种轻量级分层双监督自动编码器（LWDSAE）模型，该模型支持负载识别和基于置信度的协作任务。具有学习能力的客户端通过一次性微调生成新模型，通过基于信号重构偏差的置信度判断方法促进客户端模型之间的协作，并提高单个客户端的负载识别性能。此外，还开发了一种异常样本检测驱动的模型组合更新方法，以帮助每个客户端在模型数量限制下保持最佳局部性能。在两个公共数据集和实际应用上的综合评估表明，该框架在分布式持续学习场景中实现了持续的性能提升，始终优于现有方法。
