| Paper ID | MLSP-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 |
| Session | MLSP-21: Generative Neural Networks |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
