%0 Journal Article 
%A Precioso Garcelán, Daniel
%A Gómez-Ullate Oteiza, David
%T Thresholding methods in non-intrusive load monitoring
%D 2023
%@ 0920-8542
%U http://hdl.handle.net/10498/28902
%X 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.
%K Non-intrusive load monitoring (NILM)
%K Recurrent neural networks
%K Convolutional neural networks
%K Binary cross-entropy loss
%K mean squared error loss
%~ Universidad de Cádiz