Leveraging Generative AI to Learn Impact of Climate Change on Buildings Urban Areas

Abstract

Climate change, global warming, and pollution are intensifying daily. As urbanization increases, understanding the reciprocal impact between buildings and the environment becomes increasingly important. While most research on building monitoring through the Internet of Things (IoT) emphasizes energy consumption and data collection, it often overlooks the effects of outdoor environmental factors on buildings and vice versa. Additionally, existing studies frequently lack detailed reports that clarify their findings. This work aims to expand our understanding of environmental influences on buildings, indoor environments, and residents. It also seeks to generate comprehensive reports on these impacts, providing actionable recommendations to mitigate and minimize them through the use of Generative Artificial Intelligence. Specifically, we fine-tuned Large Language Models (LLMs) such as Generative Pre-trained Transformer 2 (GPT-2) and Large Language Model Meta AI 2 (LLAMA2-7b), using the Nous Research LLAMA2-7b-hf version from Hugging Face, on a custom dataset compiled from diverse online sources. Our research examines the effects of environmental factors, including temperature, humidity, and air quality, on urban buildings and indoor environments, with these models generating reports that offer practical recommendations. The generated reports offer a clear understanding of environmental impacts on buildings and suggest strategies to minimize these effects. These insights are intended to support effective urban planning and sustainable development. By implementing these recommendations or best practices, we can enhance indoor environmental quality while reducing contributions to global warming. Future work will involve continuous monitoring of buildings' indoor environments, energy consumption, and greenhouse gas (GHG) emissions, further reducing GHG emissions and addressing global warming.