@misc{10498/28902,
year = {2023},
month = {4},
url = {http://hdl.handle.net/10498/28902},
abstract = {Non-intrusive load monitoring (NILM) is the problem of predicting the status or
consumption of individual domestic appliances only from the knowledge of the
aggregated power load. NILM is often formulated as a classifcation (ON/OFF)
problem for each device. However, the training datasets gathered by smart meters
do not contain these labels, but only the electric consumption at every time interval.
This paper addresses a fundamental methodological problem in how a NILM problem is posed, namely how the diferent possible thresholding methods lead to diferent classifcation problems. Standard datasets and NILM deep learning models are
used to illustrate how the choice of thresholding method afects the output results.
Some criteria that should be considered for the choice of such methods are also proposed. Finally, we propose a slight modifcation to current deep learning models
for multi-tasking, i.e. tackling the classifcation and regression problems simultaneously. Transfer learning between both problems might improve performance on each
of them.},
organization = {Funding for open access publishing: Universidad de Cádiz/CBUA. This research has been financed in part by the Spanish Agencia Estatal de Investigación under grants PID2021-122154NB-I00 and TED2021-129455B-I00, and by a 2021 BBVA Foundation project for research in Mathematics. He also acknowledges support from the EU under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (FEDER-UCA18-108393).},
publisher = {Springer},
keywords = {Non-intrusive load monitoring (NILM)},
keywords = {Recurrent neural networks},
keywords = {Convolutional neural networks},
keywords = {Binary cross-entropy loss},
keywords = {mean squared error loss},
title = {Thresholding methods in non-intrusive load monitoring},
doi = {10.1007/s11227-023-05149-8},
author = {Precioso Garcelán, Daniel and Gómez-Ullate Oteiza, David},
}