Leveraging Generative AI to Learn Impact of ClimateChange on Buildings inUrban Areas

Climate change, global warming, and pollution are intensifying daily. As urbanization increases, understanding the reciprocal impact between buildings and the environment becomes increasingly important. Most research focuses on building monitoring using Internet of Things (IoT), such as energy consumption, data collection, etc., but still, overlooks the outdoor environmental impacts on buildings and vice versa and often lacks comprehensive reports explaining the results. 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 with the help ofGenerative 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, and generate the reports with actionable 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 following 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.