A Robust to Noise Adversarial Recurrent Model for Non-Intrusive Load Monitoring

Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios D. Doulamis; Eftychios Protopapadakis

ICASSP 2021accepted0 citations

A Robust to Noise Adversarial Recurrent Model for Non-Intrusive Load Monitoring

Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios D. Doulamis, Eftychios Protopapadakis

Abstract

The problem of separating the household aggregated power signal into its additive sub-components, called energy (power) disaggregation or Non-Intrusive Load Monitoring (NILM) can play an instrumental role as a driver towards consumer energy consumption awareness and behavioral change. In this paper, we propose EnerGAN++, an adversarially trained model for robust energy disaggregation. We propose a unified autoencoder (AE) and GAN architecture, in which the AE achieves a non-linear power signal source separation. The discriminator performs sequence classification, using a recurrent CNN to handle the temporal dynamics of an appliance energy consumption time series. Experimental results indicate the proposed method’s superiority compared to the state of the art.

BibTeX

@inproceedings{icassp2021_arobusttonoisead,
  title = {A Robust to Noise Adversarial Recurrent Model for Non-Intrusive Load Monitoring},
  author = {Maria Kaselimi and Athanasios Voulodimos and Nikolaos Doulamis and Anastasios D. Doulamis and Eftychios Protopapadakis},
  booktitle = {ICASSP 2021},
  year = {2021}
}