Quantitative analysis of performance-oriented design efficiency in early divergent parametric design of office building façade

Abstract

Optimizing early-stage building design significantly impacts energy efficiency, carbon reduction, and indoor air quality. While parametric design coupled with Multi-Objective Optimization is a prevalent approach in energy-focused preliminary design, it's important to note that the emphasis has been on parameter adjustments rather than algorithm modifications. However, altering the design algorithm can have a more fundamental influence on early-stage design, yet this area remains relatively under-researched. To aid designers in producing better performance-oriented designs at an early stage, this study proposes a set of quantitative index and methods to swiftly evaluate and compare the performance potential of design algorithms. Office passive design case studies are utilized to assess the performance of two common divergent design algorithms. Through solution space sampling, we analyze their respective performance boundaries and distributions by conducting sampling analysis and performance optimization for each algorithm. Notably, energy consumption differences can reach up to 5.07 kWh/ (m2·a), while sUDI variations may amount to 25.2%, and these variances are contingent upon climate zones. Furthermore, the efficiency and convergence speed of these design algorithms during optimization processes were quantitatively compared. Results indicate that optimal energy consumption can vary by up to 2.13 kWh/ (m2·a), with a time cost ranging from three to four times for approximating an optimal solution. The key contribution lies in providing designers with a rapid evaluation methods for performance-oriented comparison for different early design algorithms.