Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

TL;DR

This paper introduces DualNILM, a deep multi-task learning framework using Transformer architecture to improve energy disaggregation in smart homes, especially with BTM energy sources like solar panels and batteries.

Contribution

The paper proposes a novel Transformer-based multi-task learning approach for simultaneous appliance state recognition and injected energy identification in NILM, addressing BTM energy challenges.

Findings

DualNILM outperforms conventional NILM methods in dual tasks

The framework effectively captures multiscale temporal dependencies

Synthetic PV-augmented datasets are provided for further research

Abstract

Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the increasing adoption of behind-the-meter (BTM) energy sources such as solar panels and battery storage poses new challenges for conventional NILM methods that rely solely on at-the-meter data. The energy injected from the BTM sources can obscure the power signatures of individual appliances, leading to a significant decrease in NILM performance. To address this challenge, we present DualNILM, a deep multi-task learning framework designed for the dual tasks of appliance state recognition and injected energy identification. Using a Transformer-based architecture that integrates sequence-to-point and sequence-to-sequence strategies, DualNILM effectively captures multiscale temporal dependencies in the aggregate power consumption patterns, allowing for accurate appliance state recognition and energy injection identification. Extensive evaluation on self-collected and synthesized datasets demonstrates that DualNILM maintains an excellent performance for dual tasks in NILM, much outperforming conventional methods. Our work underscores the framework's potential for robust energy disaggregation in modern energy systems with renewable penetration. Synthetic photovoltaic augmented datasets with realistic injection simulation methodology are open-sourced at https://github.com/MathAdventurer/PV-Augmented-NILM-Datasets.