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.
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Acknowledgement
This research was supported by the CITIES project (No. 1035-00027B) and the HEAT 4.0 project (No. 8090-00046B) funded by IFD.
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Liu, X., Lai, Z., Wang, X., Huang, L., Nielsen, P.S. (2020). A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_83
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DOI: https://doi.org/10.1007/978-3-030-63823-8_83
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