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

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  • Zhao, Yang
  • Li, Tingting
  • Zhang, Xuejun
  • Zhang, Chaobo

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.

Suggested Citation

  • Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    • Handle: RePEc:eee:rensus:v:109:y:2019:i:c:p:85-101
    DOI: 10.1016/j.rser.2019.04.021
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    as
    1. Silke Friedrich, 2013. "Energy Efficiency in Buildings in EU Countries," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 11(2), pages 57-59, 07.
    2. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    3. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    4. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    5. Wang, Shengwei & Cui, Jingtan, 2005. "Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method," Applied Energy, Elsevier, vol. 82(3), pages 197-213, November.
    6. Silke Friedrich, 2013. "Energy Efficiency in Buildings in EU Countries," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 11(02), pages 57-59, July.
    7. repec:ces:ifodic:v:11:y:2013:i:2:p:19094737 is not listed on IDEAS
    8. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    9. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    10. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    11. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
    12. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    13. Chung, William, 2012. "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 95(C), pages 45-49.
    14. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    15. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.
    16. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    17. Cai, Baoping & Liu, Yonghong & Fan, Qian & Zhang, Yunwei & Liu, Zengkai & Yu, Shilin & Ji, Renjie, 2014. "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network," Applied Energy, Elsevier, vol. 114(C), pages 1-9.
    18. Najafi, Massieh & Auslander, David M. & Bartlett, Peter L. & Haves, Philip & Sohn, Michael D., 2012. "Application of machine learning in the fault diagnostics of air handling units," Applied Energy, Elsevier, vol. 96(C), pages 347-358.
    19. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    20. Wang, Liping & Greenberg, Steve & Fiegel, John & Rubalcava, Alma & Earni, Shankar & Pang, Xiufeng & Yin, Rongxin & Woodworth, Spencer & Hernandez-Maldonado, Jorge, 2013. "Monitoring-based HVAC commissioning of an existing office building for energy efficiency," Applied Energy, Elsevier, vol. 102(C), pages 1382-1390.
    21. Lee, Tzong-Shing & Lu, Wan-Chen, 2010. "An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers," Applied Energy, Elsevier, vol. 87(11), pages 3486-3493, November.
    22. Yu, Zhun (Jerry) & Haghighat, Fariborz & Fung, Benjamin C.M. & Morofsky, Edward & Yoshino, Hiroshi, 2011. "A methodology for identifying and improving occupant behavior in residential buildings," Energy, Elsevier, vol. 36(11), pages 6596-6608.
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