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• W3096456058 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." @default.
• W3096456058 created "2020-11-09" @default.
• W3096456058 creator A5039299847 @default.
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• W3096456058 date "2020-10-28" @default.
• W3096456058 modified "2023-09-27" @default.
• W3096456058 title "NILM as a regression versus classification problem: the importance of thresholding." @default.
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• W3096456058 hasPublicationYear "2020" @default.
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