Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

Yang Zhao, Tingting Li, Xuejun Zhang and Chaobo Zhang

Renewable and Sustainable Energy Reviews, 2019, vol. 109, issue C, 85-101

Abstract: Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of building energy systems. This paper aims at making a comprehensive literature review of artificial intelligence-based fault detection and diagnosis (FDD) methods for building energy systems in the past twenty years from 1998 to 2018, summarizing the strengths and shortcomings of the existing artificial intelligence-based methods, and revealing the most important research tasks in the future. Challenges in developing FDD methods for building energy systems are discussed firstly. Then, a comprehensive literature review is made. All methods are classified into two categories, i.e. data driven-based and knowledge driven-based. The data driven-based methods are abundant, including the classification-based, unsupervised learning-based and regression-based. They showed powerful capacity in learning patterns from training data. But, they need a large amount of training data, and have problems in reliability and robustness. The knowledge driven-based methods show powerful capacity in simulating the diagnostic thinking of experts. But, they rely on expert knowledge heavily. It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.

Keywords: Fault detection; Fault diagnosis; Building energy systems; Artificial intelligence; Big data (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (63)

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DOI: 10.1016/j.rser.2019.04.021

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