A real-time abnormal operation pattern detection method for building energy systems based on association rule bases

Expert systems are effective for anomaly detection in building energy systems. However, it is usually inefficient to establish comprehensive rule bases manually for complex building energy systems. Association rule mining is available to accelerate the establishment of the rule bases due to its powerful capability of discovering rules from numerous data. This paper proposes a real-time abnormal operation pattern detection method towards building energy systems. It can benefit from both expert systems and association rule mining. Association rules are utilized to establish association rule bases of abnormal and normal operation patterns. The established rule bases are then utilized to develop an expert system for real-time detection of abnormal operation patterns. The proposed method is applied to an actual chiller plant for evaluating its performance. Results show that 15 types of known abnormal operation patterns and 11 types of unknown abnormal operation patterns are detected successfully by the proposed method.