Appliance Identification in NILM Applications by means of a Convolutional Auto-Encoder
Authors
Diego Otón, Laura De; Hernández Alonso, Álvaro; Pizarro Pérez, Daniel; Nieto Capuchino, RubénIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60395DOI: 10.1109/MetroLivEnv56897.2023.10164065
ISBN: 978-1-6654-5692-0
Publisher
IEEE
Date
2023Bibliographic citation
L. de Diego-Otón, Á. Hernández, D. Pizarro and R. Nieto, "Appliance Identification in NILM Applications by means of a Convolutional Auto-Encoder," 2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv), Milano, Italy, 2023, pp. 202-207, doi: 10.1109/MetroLivEnv56897.2023.10164065.
Document type
info:eu-repo/semantics/bookPart
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1109/MetroLivEnv56897.2023.10164065Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2023 IEEE
Access rights
info:eu-repo/semantics/openAccess
Abstract
n energy efficiency applications, Non-Intrusive
Load Monitoring techniques (NILM) are typically used to deduce
which electrical loads are being used in a building at a given
time. The identification of household appliances, in particular
manually operated ones, is relevant information that can also be
applied to infer the routines of tenants in Active and Assisted
Living environments (AAL). These tools and applications
are becoming increasingly interesting, especially in Western
countries, where the ageing population is putting a strain on
public social and health services. In this context, this work aims
to classify the on/off events of the devices considered in the
BLUED database. For this purpose, an architecture is presented,
consisting of a Convolutional Auto-Encoder (CAE) followed by
a classifier neural network. The CAE is used to implement
a dimensionality reduction process after the encoder. Input
data are formatted as images, created with extracted sections
of the high-frequency electric current signal captured around
the switching events. It is noteworthy that this dimensionality
reduction also allows a decrease in the computational load of the
classifier. Regarding the CAE functionality, the reconstruction
error reaches a value of 1.579 · 10−3, whereas in the validation
stage a weighted average classification F1-score of 87 % is
obtained for the whole architecture.
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