A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS)

ISBN

9789897584268

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

288-295

Název nakladatele

SciTePress

Místo vydání

Setubal, Portugal

Místo konání akce

Prague, Czech Republic

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000615960700030