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A Contextual Anomaly Detection Framework for Energy Smart Meter Data

Liu, Xiufeng; lai, zhichen; wang, xin; Huang, Lizhen; Nielsen, Per Sieverts
Chapter
Published version
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Liu (Locked)
URI
https://hdl.handle.net/11250/2781356
Date
2009
Metadata
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  • Institutt for vareproduksjon og byggteknikk [1147]
  • Publikasjoner fra CRIStin - NTNU [41869]
Original version
10.1007/978-3-030-63823-8_83
Abstract
Monitoring abnormal energy consumption is helpful for demand-side management. This paper proposes a framework for contextual anomaly detection (CAD) for residential energy consumption. This framework uses a sliding window approach and prediction-based detection method, along with the use of a concept drift method to identify the unusual energy consumption in different contextual environments. The anomalies are determined by a statistical method with a given threshold value. The paper evaluates the framework comprehensively using a real-world data set, compares with other methods and demonstrates the effectiveness and superiority.
Publisher
Springer
Copyright
This version of the article will not be available due to copyright restrictions by Springer

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