Integration of Artificial Intelligence (AI) in Building Performance Optimization.

Abstract

The integration of Artificial Intelligence in building performance optimization offers strong opportunities for advancing sustainability in tropical regions where high temperatures and humidity present distinct design challenges. Although interest in this field is growing, only a small number of studies focus on the practical use of AI driven parametric design tools in tropical environments. This study examines how AI algorithms, working with parametric simulation platforms such as Grasshopper and Ladybug, can optimise daylighting, thermal comfort, and energy performance in naturally ventilated buildings in Nigeria. Using machine learning models such as Artificial Neural Networks and Support Vector Machines, several building configurations were assessed across multiple performance indicators. The results show that AI based optimisation improved daylight autonomy by up to 40 percent, reduced energy consumption by 30 percent, and increased thermal comfort hours by 34 percent when compared with conventional design methods. The study concludes that AI supported parametric workflows offer a strong approach for climate responsive and energy efficient building design in data scarce tropical settings. It recommends wider adoption of AI enhanced design processes, increased investment in professional training, and the development of AI models that are tailored to tropical building conditions.