Multi-Objective Optimization of Energy Use, Thermal Comfort, and Carbon Emissions in an Underground Supermarket

This study investigates the coordinated optimization of these performance objectives for a single-level underground supermarket in Xuzhou, China. Energy use intensity (EUI), occupied-hour discomfort time ratio (DTR), and carbon emissions from retrofit-material production (A1-A3) and operational energy use (B6) were adopted as objectives.

Eleven envelope, overburden, morphology, and operational variables were evaluated using 2000 Latin hypercube samples through a Rhino-Grasshopper/Ladybug Tools-Honeybee workflow. Independent back-propagation neural-network surrogate models were combined with Morris sensitivity analysis and NSGA-III, and entropy-weight TOPSIS was used to rank Pareto solutions. The selected compromise solution reduced EUI by 50.9% and carbon emissions by 44.6%, while decreasing DTR from 49.29% to 23.96% relative to the baseline.