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Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Author

Listed:
  • Dasheng Lee

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Liyuan Chen

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

Artificial intelligence (AI) technologies have developed rapidly since 2000. Numerous academic papers have been published regarding energy efficiency improvements for air-conditioning systems. This study reviewed 12 review papers and selected 85 specific cases of applications of AI for HVAC energy usage reduction. In addition to academic studies, 31,221 patents related to HVAC energy-saving equipment filed by 11 companies were investigated. In order to analyze the large amount of data, this study developed a resource description framework (RDF) as an analysis tool. This tool was used with a natural language processing (NLP) program to compare the contents of academic papers and patents. With the automated analysis program, this study aimed to link academic research and corporate research and development, mainly the enterprise patent applications, to analyze the reasons why AI can effectively save energy. This represents a complete analysis of the current status of academic and industrial development. Six methods were identified to save energy effectively, including model-based predictive control (MPC), thermal comfort control, model-free predictive control, control optimization, multi-agent control (MAC), and knowledge-based system/rule set (KBS/RS)-based control. The energy savings of these methods were quantified to be 8.8–25.5%. These methods are widely covered by the examined corporate patent applications. After using NLP to retrieve patent keywords, the landscapes of enterprise patents were constructed and the future research directions were identified. It is concluded that 10 topics, including novel neural network designs, smartphone-assisted machine learning, and transfer learning, can be used to increase the energy-saving effects of AI and enable sustainable air-conditioning systems.

Suggested Citation

  • Dasheng Lee & Liyuan Chen, 2022. "Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects," Sustainability, MDPI, vol. 14(12), pages 1-82, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7514-:d:843415
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    References listed on IDEAS

    as
    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).
    3. Lin, Haiyang & Wang, Qinxing & Wang, Yu & Liu, Yiling & Sun, Qie & Wennersten, Ronald, 2017. "The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model," Applied Energy, Elsevier, vol. 202(C), pages 248-258.
    4. Dasheng Lee & Fu-Po Tsai, 2020. "Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner," Energies, MDPI, vol. 13(8), pages 1-25, April.
    5. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    6. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    7. Dey, Maitreyee & Rana, Soumya Prakash & Dudley, Sandra, 2021. "Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach," Applied Energy, Elsevier, vol. 298(C).
    8. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
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