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Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale

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Listed:
  • Chao Ding

    (Lawrence Berkeley National Laboratory)

  • Jing Ke

    (Lawrence Berkeley National Laboratory)

  • Mark Levine

    (Lawrence Berkeley National Laboratory)

  • Nan Zhou

    (Lawrence Berkeley National Laboratory)

Abstract

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

Suggested Citation

  • Chao Ding & Jing Ke & Mark Levine & Nan Zhou, 2024. "Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50088-4
    DOI: 10.1038/s41467-024-50088-4
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    References listed on IDEAS

    as
    1. Sang M. Lee & DonHee Lee & Youn Sung Kim, 2019. "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-11, December.
    2. Kurdgelashvili, Lado & Li, Junli & Shih, Cheng-Hao & Attia, Benjamin, 2016. "Estimating technical potential for rooftop photovoltaics in California, Arizona and New Jersey," Renewable Energy, Elsevier, vol. 95(C), pages 286-302.
    3. Constantine E. Kontokosta & Danielle Spiegel-Feld & Sokratis Papadopoulos, 2020. "The impact of mandatory energy audits on building energy use," Nature Energy, Nature, vol. 5(4), pages 309-316, April.
    4. Jung, Wooyoung & Jazizadeh, Farrokh, 2019. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions," Applied Energy, Elsevier, vol. 239(C), pages 1471-1508.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    6. Rositsa T. Ilieva & Timon McPhearson, 2018. "Social-media data for urban sustainability," Nature Sustainability, Nature, vol. 1(10), pages 553-565, October.
    7. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Sukjoon Oh & Suyeon Ham & Seongjin Lee, 2021. "Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes," Energies, MDPI, vol. 14(19), pages 1-19, October.
    9. Francesc X. Prenafeta-Boldú & Andreas Kamilaris, 2019. "AI assists in locating hidden farms," Nature Sustainability, Nature, vol. 2(4), pages 262-263, April.
    10. Benjamin K. Sovacool & Steve Griffiths, 2020. "Culture and low-carbon energy transitions," Nature Sustainability, Nature, vol. 3(9), pages 685-693, September.
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