Online Anomaly Energy Consumption Detection Using Lambda Architecture

Xiufeng Liu, Nadeem Iftikhar, Per Sieverts Nielsen, Alfred Heller

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Abstract

With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative refreshing the detection models from scalable data sets, but also real-time anomaly detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the lambda detection system.
OriginalsprogEngelsk
Titel18th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2016
RedaktørerSanjay Madria, Takahiro Hara
Antal sider14
ForlagSpringer
Publikationsdato2016
Sider193-209
ISBN (Trykt)978-3-319-43945-7
ISBN (Elektronisk)978-3-319-43946-4
DOI
StatusUdgivet - 2016
Begivenhed18th International Conference on Big Data Analytics and Knowledge Discovery - Porto, Portugal
Varighed: 6 sep. 20168 sep. 2016
Konferencens nummer: 18

Konference

Konference18th International Conference on Big Data Analytics and Knowledge Discovery
Nummer18
Land/OmrådePortugal
ByPorto
Periode06/09/1608/09/16
NavnLecture Notes in Computer Science
Nummer10.1007/978-3-319-43946-4_13
Vol/bind9829

Emneord

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