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Enhancing Energy Efficiency in Green Buildings Through Artificial Intelligence
Version 1
: Received: 20 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (15:32:44 CEST)
How to cite: Feng, Z.; Ge, M.; Meng, Q. Enhancing Energy Efficiency in Green Buildings Through Artificial Intelligence. Preprints 2024, 2024081489. https://doi.org/10.20944/preprints202408.1489.v1 Feng, Z.; Ge, M.; Meng, Q. Enhancing Energy Efficiency in Green Buildings Through Artificial Intelligence. Preprints 2024, 2024081489. https://doi.org/10.20944/preprints202408.1489.v1
Abstract
Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.
Keywords
Artificial Intelligence (AI)；Energy Efficiency；Green Building Design；Carbon Emissions Reduction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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