Online Anomaly Energy Consumption Detection Using Lambda Architecture

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

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Pages (from-to)193-209
Number of pages17
Publication statusPublished - 2016
Event18th International Conference on Big Data Analytics and Knowledge Discovery - Porto, Portugal
Duration: 5 Sept 20168 Sept 2016
Conference number: 18


Conference18th International Conference on Big Data Analytics and Knowledge Discovery
Internet address


  • Anomaly detection
  • Real-time
  • Lambda architecture
  • Data mining


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