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- W3217439573 abstract "Energy disaggregation allows identifying individual consumption of different appliances using only the aggregated signal measured from a single point. This work proposes a neural network trained with wavelets reduced data to perform energy disaggregation. Besides the disaggregation, usually a binary answer by identifying the appliance activation moment, we are interested in estimating the appliance’s consumption value. We consider the U.K.-DALE dataset to perform our experiments, containing data from different appliances of five houses from England. Using our strategy, compared with another well-established work, we achieved improvements per appliance of 11.4% (estimated accuracy) in the disaggregation process and 27.8% ( <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$F_1$</tex-math></inline-formula> -score) in the appliance’s consumption value. Our main contribution was to identify satisfactorily that the coefficients of approximation of the wavelet transform are enough to estimate the individual consumption of household appliances." @default.
- W3217439573 created "2021-12-06" @default.
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- W3217439573 date "2022-10-01" @default.
- W3217439573 modified "2023-10-16" @default.
- W3217439573 title "An Energy Disaggregation Approach Based on Deep Neural Network and Wavelet Transform" @default.
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- W3217439573 doi "https://doi.org/10.1109/tii.2021.3130416" @default.
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