This compendium showcases student projects from BPS5231: AI for Sustainable Building Design at the National University of Singapore. The projects span four themes (Energy, Urban Environmental Well-Being, Multi-Objective Optimization, and Urban Design) demonstrating how artificial intelligence, machine learning, and data-driven modeling can accelerate sustainable design from building performance prediction to urban-scale spatial analysis. Students explore AI-assisted workflows for energy consumption forecasting, retrofit evaluation, multi-objective optimization balancing energy use with comfort and cost, microclimate-aware street design, and equitable access to urban green spaces. Each project emphasizes practical applications of surrogate modeling, generative AI, and predictive analytics to support evidence-based decision-making in architecture, engineering, and urban planning, making sustainable design faster, more scalable, and more responsive to climate and human needs.
