Poster abstract: Synthetic dataset generation for non-intrusive load monitoring in commercial buildings

In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings. Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research. One of the main difficulties is that the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In order to circumvent the issues caused by the lack of data available, we propose a model for generating realistic synthetic current waveforms by making use of both publicly available datasets and our private dataset that is collected from real commercial buildings. Our primarily experiments show that the generated data ressemble real datasets.