Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving

Yabin Guo, Zehan Tan, Huanxin Chen, Guannan Li, Jiangyu Wang, Ronggeng Huang, Jiangyan Liu and Tanveer Ahmad

Applied Energy, 2018, vol. 225, issue C, 732-745

Abstract: The fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance.

Keywords: Deep learning; Deep belief network; Fault diagnosis; Energy saving; Variable refrigerant flow air-conditioning system (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:225:y:2018:i:c:p:732-745

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DOI: 10.1016/j.apenergy.2018.05.075

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