Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data

TL;DR

This paper introduces a federated sequence-to-sequence learning approach utilizing weather data to improve load disaggregation in low-resolution smart meter data, addressing data privacy and heterogeneity challenges.

Contribution

It presents a novel federated learning model that leverages weather data for NILM with low-resolution data, enabling appliance load disaggregation without sharing raw data.

Findings

FL model effectively handles data heterogeneity

Weather data significantly improves disaggregation accuracy

Approach works with hourly low-resolution data

Abstract

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.