期刊
ENERGY AND BUILDINGS
卷 215, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2020.109864
关键词
Deep learning; Quantile regression; Anomaly detection; Building energy management
资金
- National Natural Science Foundation of China [51876070, 51576074]
With the development in information technologies, today's building energy consumption can be well monitored by the building energy management systems. However, in most real applications there is no clear definition of abnormal building energy consumption. To overcome this limitation, this work proposes a novel deep learning based unsupervised anomaly detection framework that includes recurrent neural networks and quantile regression. Moreover, this framework is able to produce a prediction interval to detect and evaluate abnormal building energy consumption. The framework has been applied to analyze the energy data collected from three different residential houses, and anomaly detection results are evaluated by the quantile regression range. The research results can provide promising solutions for building managers to detect abnormal energy performance, and is also valuable to assess the level of anomalies and spot opportunities in energy conservation. (C) 2020 Elsevier B.V. All rights reserved.
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