%0 Journal Article
%K Energy efficiency
%K Energy modeling
%K Artificial Intelligence
%A Chao Ding
%A Jing Ke
%A Mark D Levine
%A Nan Zhou
%B Nature Communications
%D 2024
%G eng
%R https://doi.org/10.1038/s41467-024-50088-4
%T Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale
%U https://www.nature.com/articles/s41467-024-50088-4
%V 15
%8 07/2024
%X <p>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.</p>