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arXiv · 2010.16050

NILM as a regression versus classification problem: the importance of thresholding

Daniel Precioso·David Gómez-Ullate
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Abstract

Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresponding classification problem is a derived one, since it requires a conversion from the power signal to the status of each device by a thresholding method. We treat three different thresholding methods to perform this task, discussing their differences on various devices from the UK-DALE dataset. We analyze the performance of deep learning state-of-the-art architectures on both the regression and classification problems, introducing criteria to select the most convenient thresholding method.

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Daniel Precioso, David Gómez-Ullate. 2020-10-28. NILM as a regression versus classification problem: the importance of thresholding. https://doi.org/10.1007/s11227-023-05149-8

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