dc.contributor.author | Liu, Xiufeng | |
dc.contributor.author | lai, zhichen | |
dc.contributor.author | wang, xin | |
dc.contributor.author | Huang, Lizhen | |
dc.contributor.author | Nielsen, Per Sieverts | |
dc.date.accessioned | 2021-09-24T10:19:25Z | |
dc.date.available | 2021-09-24T10:19:25Z | |
dc.date.created | 2021-03-16T15:29:53Z | |
dc.date.issued | 2009 | |
dc.identifier.isbn | 3642026702 | |
dc.identifier.uri | https://hdl.handle.net/11250/2781356 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Communications in Computer and Information Science | |
dc.title | A Contextual Anomaly Detection Framework for Energy Smart Meter Data | en_US |
dc.type | Chapter | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | This version of the article will not be available due to copyright restrictions by Springer | en_US |
dc.source.pagenumber | 733-742 | en_US |
dc.identifier.doi | 10.1007/978-3-030-63823-8_83 | |
dc.identifier.cristin | 1898414 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |