@inproceedings{ab53ffa8240e4d50bd051d2d88697cb1,
title = "A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream",
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.",
author = "Xiufeng Liu and Zhichen Lai and Xin Wang and Lizhen Huang and Nielsen, {Per Sieverts}",
year = "2020",
doi = "10.1007/978-3-030-63823-8_83",
language = "English",
isbn = "978-3-030-63822-1",
series = "Neural Information Processing - Letters and Reviews",
pages = "733--742",
booktitle = "Proceedings of the International Conference on Neural Information Processing",
publisher = "Springer",
note = "International Conference on Neural Information Processing (ICONIP 2020) ; Conference date: 18-11-2020 Through 22-11-2020",
}
