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- W2938814897 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." @default.
- W2938814897 created "2019-04-25" @default.
- W2938814897 creator A5012436877 @default.
- W2938814897 creator A5017329414 @default.
- W2938814897 creator A5025094844 @default.
- W2938814897 creator A5074793684 @default.
- W2938814897 date "2019-07-01" @default.
- W2938814897 modified "2023-10-10" @default.
- W2938814897 title "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future" @default.
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