2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-21.4
Paper Title A ROBUST TO NOISE ADVERSARIAL RECURRENT MODEL FOR NON-INTRUSIVE LOAD MONITORING
Authors Maria Kaselimi, National Technical University of Athens, Greece; Athanasios Voulodimos, University of West Attica, Greece; Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, National Technical University of Athens, Greece
SessionMLSP-21: Generative Neural Networks
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.