Moure-Garrido, Marta; Campo-Vázquez, Celeste; García-Rubio, Carlos Anomalies detection using entropy in household energy consumption data Conference Intelligent Environments 2020 Workshop Proceedings of the 16th International Conference on Intelligent Environments, 2020, ISBN: 978-1-64368-090-3. Abstract | Links | BibTeX | Tags: anomaly, cynamon, entropy, household energy consumption2020
@conference{campo016,
title = {Anomalies detection using entropy in household energy consumption data },
author = {Marta Moure-Garrido and Celeste Campo-Vázquez and Carlos García-Rubio},
url = {https://ebooks.iospress.nl/publication/54775},
doi = {10.3233/AISE200055},
isbn = {978-1-64368-090-3},
year = {2020},
date = {2020-05-04},
urldate = {2020-05-04},
booktitle = {Intelligent Environments 2020 Workshop Proceedings of the 16th International Conference on Intelligent Environments},
pages = {311-320},
abstract = {The growing boom in smart grids and home automation makes possible
to obtain information of household energy consumption. In this work, we study if
entropy is a good mechanism to detect anomalies in household energy consumption traces. We propose an entropy algorithm based on windowing the temporal
series of energy consumption. We select a trace with a duration of 3 months from
the REFIT project household energy consumption data set, available open access.
Entropy can adapt to changes in consumption in this trace, by learning and forgetting patterns dynamically. Although entropy is a promising technique and it has
many advantages, as the traces in this data set are not sufficiently labeled to check
the correct functioning of the algorithms, we propose to further validate the results
using synthetic traces.},
keywords = {anomaly, cynamon, entropy, household energy consumption},
pubstate = {published},
tppubtype = {conference}
}
to obtain information of household energy consumption. In this work, we study if
entropy is a good mechanism to detect anomalies in household energy consumption traces. We propose an entropy algorithm based on windowing the temporal
series of energy consumption. We select a trace with a duration of 3 months from
the REFIT project household energy consumption data set, available open access.
Entropy can adapt to changes in consumption in this trace, by learning and forgetting patterns dynamically. Although entropy is a promising technique and it has
many advantages, as the traces in this data set are not sufficiently labeled to check
the correct functioning of the algorithms, we propose to further validate the results
using synthetic traces.
Publications
Anomalies detection using entropy in household energy consumption data Conference Intelligent Environments 2020 Workshop Proceedings of the 16th International Conference on Intelligent Environments, 2020, ISBN: 978-1-64368-090-3.2020