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AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects

Author

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  • Huakun Huang

    (School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China)

  • Dingrong Dai

    (School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China)

  • Longtao Guo

    (School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China)

  • Sihui Xue

    (School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China)

  • Huijun Wu

    (School of Civil Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

Reducing carbon emissions from buildings is crucial to achieving global carbon neutrality targets. However, the building sector faces various challenges, such as low accuracy in forecasting, lacking effective methods of measurements and accounting in terms of energy consumption and emission reduction. Fortunately, relevant studies demonstrate that artificial intelligence (AI) and big data technologies could significantly increase the accuracy of building energy consumption prediction. The results can be used for building operation management to achieve emission reduction goals. For this, in this article, we overview the existing state-of-the-art methods on AI and big data for building energy conservation and low carbon. The capacity of machine learning technologies in the fields of energy conservation and environmental protection is also highlighted. In addition, we summarize the existing challenges and prospects for reference, e.g., in the future, accurate prediction of building energy consumption and reasonable planning of human behavior in buildings will become promising research directions.

Suggested Citation

  • Huakun Huang & Dingrong Dai & Longtao Guo & Sihui Xue & Huijun Wu, 2023. "AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12332-:d:1216481
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