Supervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

© 2018, Springer Nature Switzerland AG. Households are responsible for more than 40% of the global electricity consumption [7]. The analysis of this consumption to find unexpected behaviours could have a great impact on saving electricity. This research presents an experimental study of supervised and unsupervised neural networks for anomaly detection in electrical consumption. Multilayer perceptrons and autoencoders are used for each approach, respectively. In order to select the most suitable neural model in each case, there is a comparison of various architectures. The proposed methods are evaluated using real-world data from an individual home electric power usage dataset. The performance is compared with a traditional statistical procedure. Experiments show that the supervised approach has a significant improvement in anomaly detection rate. We evaluate different possible feature sets. The results demonstrate that temporal data and measures of consumption patterns such as mean, standard deviation and percentiles are necessary to achieve higher accuracy.
Original languageAmerican English
Title of host publicationSupervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption
Pages98-109
Number of pages87
ISBN (Electronic)9783030044909
DOIs
StatePublished - 1 Jan 2018
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11288 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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García, J., Zamora, E., & Sossa, H. (2018). Supervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption. In Supervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption (pp. 98-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11288 LNAI). https://doi.org/10.1007/978-3-030-04491-6_8