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Energy consumption of heat pump system accounts for a large part of the total building energy consumption, and the energy-saving operation of heat pump system has always been the focus of researchers. A promising solution to tackling energy wastes during system operations is anomaly detection. In this study, we propose an anomaly detection method for GSHP system in a public building based on mode decomposition based LSTM and statistical modeling method Grubbs' test. The system energy consumption is predicted using mode decomposition based LSTM algorithm, and the difference between predicted value and actual value is used to detect the abnormal system energy consumption by Grubbs' test. Results show that detected anomalies can be summarily divided into three categories (parabola anomaly, abrupt anomaly and time related anomaly) depending on their characteristics, and the rationality of detected anomalies are evaluated through field investigation and expert knowledge. This work is enlightening and indicates that the proposed method would efficiently detect the abnormal performance of GSHP system, and find out unreasonable operating patterns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]