Adversarial Energy Disaggregation

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 the 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 article, we propose a novel method named adversarial energy disaggregation 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 shared 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.

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

“…Moreover, a novel approach called adversarial energy disaggregation based on deep neural networks DNNs was introduced. 25 This method was applied to different appliances and compared with other methods in previous research. A new methodology for NILM based on identifying home appliances' activation states using a fully convolutional deep neural network was presented before.…”

“…Also, the NILM method based on CNN was proposed 24 to identify the power consumption load and its working conditions to profile residential consumer behavior. Moreover, a novel approach called adversarial energy disaggregation based on deep neural networks DNNs was introduced 25 . This method was applied to different appliances and compared with other methods in previous research.…”