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

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  1. Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
  2. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
  3. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
  4. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
  5. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
  6. Jun Kwon Hwang & Patrick Nzivugira Duhirwe & Geun Young Yun & Sukho Lee & Hyeongjoon Seo & Inhan Kim & Mat Santamouris, 2020. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
  7. Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).
  8. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
  9. Zhang, Boyan & Rezgui, Yacine & Luo, Zhiwen & Zhao, Tianyi, 2024. "Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors," Energy, Elsevier, vol. 313(C).
  10. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
  11. Jeong, Gil & Lee, Je Hyung & Choi, Hyung Won & Park, Hee Woong & Kim, Hyun Jong & Seo, Beom Soo & Chin, Simon & Kang, Yong Tae, 2025. "Deep learning-based prediction of oil reversal in R290 heat pump systems," Energy, Elsevier, vol. 320(C).
  12. 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.
  13. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
  14. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
  15. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
  16. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
  17. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  18. Zhang, Liang & Leach, Matt & Chen, Jianli & Hu, Yuqing, 2023. "Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings," Energy, Elsevier, vol. 263(PB).
  19. Ching-Jui Tien & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2022. "Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems," Energies, MDPI, vol. 15(11), pages 1-13, May.
  20. Jie Yang & Zhimeng Dong & Huihan Yang & Yanyan Liu & Yunjie Wang & Fujiang Chen & Haifei Chen, 2022. "Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner," Energies, MDPI, vol. 15(10), pages 1-24, May.
  21. Guangxun E & He Gao & Youfu Lu & Xuehan Zheng & Xiaoying Ding & Yuanhao Yang, 2023. "A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction," Energies, MDPI, vol. 16(20), pages 1-21, October.
  22. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
  23. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
  24. Guo, Yabin & Li, Yuduo & Li, Weilin, 2023. "On-site fault experiment and diagnosis research of the carbon dioxide transcritical heat pump system for energy saving," Energy, Elsevier, vol. 274(C).
  25. Fan, Cheng & Wu, Qiuting & Zhao, Yang & Mo, Like, 2024. "Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance," Applied Energy, Elsevier, vol. 356(C).
  26. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
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