2016
Anomaly Detection in Smart Grid Data: An Experience Report
ROSSI, Bruno, Stanislav CHREN, Barbora BÜHNOVÁ a Tomáš PITNERZákladní údaje
Originální název
Anomaly Detection in Smart Grid Data: An Experience Report
Autoři
ROSSI, Bruno (380 Itálie, domácí), Stanislav CHREN (703 Slovensko, domácí), Barbora BÜHNOVÁ (203 Česká republika, domácí) a Tomáš PITNER (203 Česká republika, domácí)
Vydání
Budapest, The 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), od s. 2313-2318, 6 s. 2016
Nakladatel
IEEE
Další údaje
Jazyk
angličtina
Typ výsledku
Stať ve sborníku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Forma vydání
elektronická verze "online"
Kód RIV
RIV/00216224:14330/16:00090404
Organizační jednotka
Fakulta informatiky
ISBN
978-1-5090-1897-0
UT WoS
000402634702033
Klíčová slova anglicky
Smart Grids; Smart Meters; Anomaly Detection; Clustering; Frequent Itemset Mining
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 13. 5. 2020 19:37, RNDr. Pavel Šmerk, Ph.D.
Anotace
V originále
In recent years, we have been witnessing profound transformation of energy distribution systems fueled by Information and Communication Technologies (ICT), towards the so called Smart Grid. However, while the Smart Grid design strategies have been studied by academia, only anecdotal guidance is provided to the industry with respect to increasing the level of grid intelligence. In this paper, we report on a successful project in assisting the industry in this way, via conducting a large anomaly-detection study on the data of one of the power distribution companies in the Czech Republic. In the study, we move away from the concept of single events identified as anomaly to the concept of collective anomaly, that is itemsets of events that may be anomalous based on their patterns of appearance. This can assist the operators of the distribution system in the transformation of their grid to a smarter grid. By analyzing Smart Meters data streams, we used frequent itemset mining and categorical clustering with clustering silhouette thresholding to detect anomalous behaviour. As the main result, we provided to stakeholders both a visual representation of the candidate anomalies and the identification of the top-10 anomalies for a subset of Smart Meters.
Návaznosti
MUNI/A/0997/2016, interní kód MU |
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