Ding Chao, Ke Jing, Levine Mark, Granderson Jessica, Zhou Nan
Energy Technologies Area, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA.
Nat Commun. 2024 Jul 14;15(1):5916. doi: 10.1038/s41467-024-50088-4.
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.
人工智能已成为一种提高生产力和改善生活质量的技术。然而，其在建筑能源效率提升和碳排放减少方面的作用尚未得到系统研究。本研究评估了人工智能在美国中型办公楼所在建筑领域的潜力。开发了一种方法来评估和量化潜在的减排量。确定的关键领域包括设备、人员占用影响、控制与运营以及设计与施工。使用六种情景来估算代表性气候区的能源和排放节省量。我们在此表明，人工智能可以降低成本溢价，提高高能效和净零能耗建筑的普及率。到2050年，采用人工智能可将能源消耗和碳排放降低约8%至19%。与能源政策和低碳发电相结合，与2050年的照常营业情景相比，可以将能源消耗降低约40%，碳排放降低90%。
