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@inproceedings{1621536,
author = {Rossi, Bruno},
address = {Setubal, Portugal},
booktitle = {Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS)},
doi = {http://dx.doi.org/10.5220/0009396402880295},
editor = {Gary Wills, Péter Kacsuk, and Victor Chang},
keywords = {Smart Grids; Smart Meters; Anomaly Detection; Power Consumption; Replication Study},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Setubal, Portugal},
isbn = {978-989-758-426-8},
pages = {288-295},
publisher = {SciTePress},
title = {A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection},
url = {https://www.scitepress.org/PublicationsDetail.aspx?ID=38neH0VRHoA=&t=1},
year = {2020}
}
TY - JOUR
ID - 1621536
AU - Rossi, Bruno
PY - 2020
TI - A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection
PB - SciTePress
CY - Setubal, Portugal
SN - 9789897584268
KW - Smart Grids
KW - Smart Meters
KW - Anomaly Detection
KW - Power Consumption
KW - Replication Study
UR - https://www.scitepress.org/PublicationsDetail.aspx?ID=38neH0VRHoA=&t=1
N2 - Anomaly detection plays a significant role in the area of Smart Grids: many algorithms were devised and applied, from intrusion detection to power consumption anomalies identification. In this paper, we focus on detecting anomalies from smart meters power consumption data traces. The goal of this paper is to replicate to a much larger dataset a previously proposed approach by Chou and Telaga (2014) based on ARIMA models. In particular, we investigate different model training approaches and the distribution of anomalies, putting forward several lessons learned. We found the method applicable also to the larger dataset. Fine-tuning the parameters showed that adopting an accumulating window strategy did not bring benefits in terms of RMSE. While a 2s rule seemed too strict for anomaly identification for the dataset.
ER -
ROSSI, Bruno. A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection. Online. In Gary Wills, Péter Kacsuk, and Victor Chang. \textit{Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS)}. Setubal, Portugal: SciTePress, 2020, p.~288-295. ISBN~978-989-758-426-8. Available from: https://dx.doi.org/10.5220/0009396402880295.
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