2020/07/20 by Alon Harell, Richard Jones, Harell, Alon +5
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Image Enhancement Techniques #IoT-based Smart Home Systems #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Smart Grid Energy Management #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2007.13645
openalex publication_date 2020/07/20 · openalex created_date 2020/07/29 · openalex updated_date 2026/07/28
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, PowerGAN, is based on conditional, progressively growing, 1-D Wasserstein generative adversarial network (GAN). Using PowerGAN, we are able to synthesise truly random and realistic appliance power data signatures. We evaluate the samples generated by PowerGAN in a qualitative way as well as numerically by using traditional GAN evaluation methods such as the Inception score.