Sustainability-Oriented Multi-Objective Optimization Design of Service Area Buildings Configured with Energy-Saving Glass Based on NSGA-II

Building energy consumption accounts for a significant proportion of total societal energy consumption, and reducing building energy consumption is critical to the global mission of reducing emissions. Windows are regarded as the least energy-efficient component of a building’s envelope. This study examines service-area buildings fitted with high-performance glass in Chinese cities across various climates and employs the non-dominated sorting genetic algorithm II (NSGA-II) genetic algorithm for multi-objective optimization. In considering design variables such as building orientation and wall insulation, advanced passive design strategies, including electrochromic and aerogel glass, are incorporated into the optimization process to minimize construction costs and operational carbon emissions. Sensitivity analyses were conducted to evaluate the impact of each design variable on building operational carbon emissions. The optimal solution within the Pareto optimal set was further evaluated using the technique for order preference by similarity to ideal solution (TOPSIS) decision-making method, and the preferred energy-saving solution was quantitatively analyzed. The results indicate that optimization leads to a reduction of approximately 7.70–10.50% in annual operational carbon emissions for service-area buildings across different regions, compared to the base case, with a payback period ranging from 4.90 to 13.56 years. The proposed method contributes to sustainable building design by jointly quantifying carbon-emission reduction, construction cost, and payback period, thereby supporting climate-responsive and economically feasible low-carbon envelope decisions for service-area buildings.