用联邦学习在低采样率数据上实现12类电器的精准用电分解。
Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data
- 基于序列到序列的联邦学习框架，无需共享原始数据。
- 结合天气数据，在每小时采样下准确识别高/低功耗电器。
- 有效应对数据异质性，适合隐私敏感的智能家居场景。
非侵入式负载监测（NILM）的重要性日益凸显，因其能提升能源意识并为能源计划设计提供洞察。现有方法多依赖高采样率复杂信号及专用设备，且聚焦高功耗电器，限制了在真实场景中的应用，尤其当智能电表仅提供家庭级低分辨率有功功率读数时。本文提出一种新方法，利用易获取的气象数据，在极低采样率（每小时）条件下对总计12类电器（涵盖高/低功耗）实现负载分解。我们构建了一个基于序列到序列模型的联邦学习（FL）框架——L2GD，实现无需数据共享的负载分解。实验表明，该框架能有效处理统计异质性，避免过拟合问题。引入气象数据显著提升了NILM性能。
原文摘要 · Abstract (English)
The importance of Non-Intrusive Load Monitoring (NILM) has been increasingly recognized, given that NILM can enhance energy awareness and provide valuable insights for energy program design. Many existing NILM methods often rely on specialized devices to retrieve high-sampling complex signal data and focus on the high consumption appliances, hindering their applicability in real-world applications, especially when smart meters only provide low-resolution active power readings for households. In this paper, we propose a new approach using easily accessible weather data to achieve load disaggregation for a total of 12 appliances, encompassing both high and low consumption, in scenarios with very low sampling rates (hourly). Moreover, We develop a federated learning (FL) model that builds upon a sequence-to-sequence model to fulfil load disaggregation without data sharing. Our experiments demonstrate that the FL framework - L2GD can effectively handle statistical heterogeneity and avoid overfitting problems. By incorporating weather data, our approach significantly improves the performance of NILM.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。