Research on Energy Consumption, Thermal Comfort, Economy, and Carbon Emissions of Residential Buildings Based on Transformer+NSGA-III Multi-Objective Optimization Algorithm

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R2 ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization.