2018/02/26 by Simon Henriet, Umut Şimşekli, Henriet, Simon +7
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Building Energy and Comfort Optimization #FOS: Computer and information sciences #Other Computer Science (cs.OH) #Smart Grid Energy Management #cs.OH
paper · pdf · doi:10.48550/arxiv.1803.00515
Submitted to Energy and Buildings, Elsevier
arxiv created 2018/02/26 · openalex publication_date 2018/02/26 · arxiv updated 2018/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 causing existing NILM approaches to fail. On the other hand, 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 this study, we aim at addressing these issues. We first present an extensive statistical analysis of both commercial and residential measurements from public and private datasets and show important differences. Secondly, we develop an algorithm for generating synthetic current waveforms. We then demonstrate using real measurement and quantitative metrics that both our device model and our simulations are realistic and can be used to evaluate NILM algorithms. Finally, to encourage research on commercial buildings we release a synthesized dataset.