Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to imp...
详细信息
Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study pro-poses a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algo-rithm with supervisedalgorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algo-rithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy con-sumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.
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