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Research Article

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

Chaobo Zhang1, Yang Zhao1( ), Yangze Zhou2, Xuejun Zhang1, Tingting Li1
Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China
Chu Kochen Honors College, Zhejiang University, Hangzhou, China
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Abstract

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.

Keywords

anomaly detectionbuilding energy systemsbuilding energy conservationexpert systemsassociation rule mining

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Building Simulation
Volume 15 Issue 1,
January 2022
Pages 69-81
DOI: 10.1007/s12273-021-0791-x
Cite this article:
Zhang C, Zhao Y, Zhou Y, et al. A real-time abnormal operation pattern detection method for building energy systems based on association rule bases. Building Simulation, 2022, 15(1): 69-81. https://doi.org/10.1007/s12273-021-0791-x

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Received: 27 November 2020
Revised: 02 March 2021
Accepted: 06 March 2021
Published: 04 June 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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