A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F20%3A00115322" target="_blank" >RIV/00216224:14610/20:00115322 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0009396402880295" target="_blank" >http://dx.doi.org/10.5220/0009396402880295</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0009396402880295" target="_blank" >10.5220/0009396402880295</a>
Alternative languages
Result language
angličtina
Original language name
A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS)
ISBN
9789897584268
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
288-295
Publisher name
SciTePress
Place of publication
Setubal, Portugal
Event location
Prague, Czech Republic
Event date
Jan 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000615960700030