Integrating machine learning in digital architecture: enhancing sustainable design and energy efficiency in urban environments

Abstract

The following work applies metaheuristic optimization algorithms—PSO, ACO, Genetic Algorithm, and Enhanced Colliding Bodies Optimization (ECBO)—to the optimum design of a sustainable building with respect to prominent metrics such as energy savings, improvement in indoor comfort, and reduction in carbon footprint. These algorithms are applied to a wide dataset that includes variable intensity factors such as window-to-wall variation ratio, HVAC efficiency, and integration of renewable energy. Results also proved that PSO is the fittest strategy to balance energy efficiency and sustainability, with the highest energy savings of 24.1%. Besides, PSO wasn’t just the fastest convergence rate; it also obtained a Platinum LEED certification. ACO was second in order of magnitude, with high energy savings and carbon footprint reduction values, and also obtained the Platinum LEED certificate. The results obtained for GA were positive from the occupant comfort point of view but were slower in terms of energy savings and convergence speed. In contrast, ECBO had the slowest convergence and lowest energy savings, demonstrating the limitation of the application of ECBO for large-scale multi-objective optimization. These results imply that PSO and ACO would be suitable for practical applications linked to urban sustainable design, while GA and ECBO are more suited for niche applications. The obtained results can provide useful guidelines in developing more energy-efficient and sustainable designs for architects, urban planners, and policymakers.