C. Gkoutroumpi, N. Virtsionis Gkalinikis, D. Vrakas. SGAN: Appliance signatures data generation for NILM applications using GANs, To be presented in the 12th Computing Conference 2024, London UK, (2024)
The development and evolution of advanced energy system
technologies is one of the most important goals for the global commu-
nity in recent years. In this effort, the utilization and analysis of energy
time series is of decisive importance for the understanding of energy con-
sumption and production patterns. However, access to real data may be
limited due to the sensitivity of the information and the limited amount
of data already available. This has led to the use of methods to produce
artificial data in order to enrich existing datasets. Generative Adversarial
Networks or GANs are an approach to generative modeling using deep
learning methods based on the logic of adversarial learning, and consist of
two adversarial neural networks, a generator and a discriminator, which
work together to produce realistic and unbiased data. The subject of the
current paper is the creation of a GAN pipeline capable of producing
power time series that resemble those observed in the real world, pre-
serving the main characteristics and diversity of the observed electrical
devices. The proposed method shows promising results, outperforming
other state-of-the-art models in two calculated metrics.
