Enhancing Energy Efficiency in Green Buildings through Artificial Intelligence

Authors

  • Zhang Feng School of Civil Engineering, Southeast University, Nanjing, China
  • Minyue Ge School of Architecture, University of Southern California, Los Angeles, United States
  • Qian Meng School of Architecture and Design, University of Technology Sydney, Sydney, Australia

DOI:

https://doi.org/10.5281/zenodo.13948759

Keywords:

artificial intelligence (ai), energy efficiency, green building design, carbon emissions reduction

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.

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Published

2024-09-30

How to Cite

Zhang Feng, Minyue Ge, & Qian Meng. (2024). Enhancing Energy Efficiency in Green Buildings through Artificial Intelligence. Applied Science and Engineering Journal for Advanced Research, 3(5), 10–17. https://doi.org/10.5281/zenodo.13948759