Adversarial Energy Disaggregation for Non-intrusive Load Monitoring

TL;DR

This paper introduces a novel adversarial neural network approach for energy disaggregation in NILM, improving accuracy by learning appliance-specific signatures and multimode structures, and achieving state-of-the-art results.

Contribution

It presents the first application of adversarial learning to NILM, enhancing energy disaggregation accuracy with a generator-discriminator framework.

Findings

Achieves state-of-the-art disaggregation performance on real datasets.

Effectively captures appliance-specific multimode energy signatures.

Outperforms traditional and existing deep learning methods.

Abstract

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. {NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy).} Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this paper, we propose a novel method named adversarial energy disaggregation (AED) based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shard representations for different appliances, but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.