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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.
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. {NILM a
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are al
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a households electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending ene
Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances. This paper proposes an event-driven Factorial Hidden Markov m
Non-intrusive load monitoring (NILM) is essential for understanding customers power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data con