High-rise developments frequently exert adverse impacts on the outdoor wind environment, leading to a deterioration in the overall quality of urban surroundings and a reduction in the comfort levels of residents. This study systematically investigates typical high-rise settlements in Xuzhou City and proposes optimization strategies to address wind environment issues through an in-depth analysis of building planning parameters. Utilizing Computational Fluid Dynamics (CFD) simulations, the research identifies key wind-related challenges associated with high-rise buildings in representative settlements. The study comprehensively examines the effects of building height, width, orientation, spacing, and layout on the outdoor wind field, progressing from individual building units to clusters. Based on these findings, optimization strategies are formulated and validated through CFD simulations conducted on a representative high-rise settlement in Xuzhou. The results reveal that typical high-rise buildings in Xuzhou exhibit a height of 54 m, a width of 48 m, and an orientation ranging from 15° to 30° southeast. The front-to-rear building spacing is approximately 1.44 times the building height, with an additional 15 m spacing from mountain walls. Optimal wind conditions are achieved with a center-vacant building layout. The optimization of building form, spacing, and orientation substantially improves the outdoor wind environment by alleviating stagnant wind zones and reducing wind pressure differentials between the building fronts and rears, thereby enhancing the comfort of residents. This study provides a valuable reference for the planning and design of high-rise settlements, contributing to an improvement in urban environmental quality and the enhancement of livability.

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong’s rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%.

The roof is the part of a building that is exposed to solar radiation for the longest period, making green roofs particularly effective in reducing air conditioning energy consumption during the summer. This study aims to assess the advantages of modular green roofs in terms of energy savings and cost reduction during the summer in Xuzhou. By conducting field measurements and surveys under both air-conditioned and non-air-conditioned conditions and utilizing building energy simulation tools, the performance of green roofs with different parameters was compared. Using EnergyPlus, factors such as soil thickness, thermal conductivity, and leaf area index were simulated. The results indicated that green roofs have superior thermal performance in summer, with the daily cooling load per unit area for top-floor rooms being 1.05 kWh/m², 0.21 kWh/m² lower than that for bare roofs, achieving an energy saving rate of 16.7%. It is recommended that soil thickness not exceed 0.3 m and insulation thickness not exceed 0.05 m or be set to 0 m. Take building no. 2 of the Xuzhou material market as an example: with the optimized green roof, the energy saving rate increased to 27.0%, which is 12.4% higher than that of the original green roof. The suggested cost for modular green roofs is 204 RMB/m².

As the core space for students’ daily living and learning, the quality of the indoor wind environment and air quality in dormitory buildings is particularly critical. However, existing studies often neglect natural ventilation optimization under local climatic conditions and the multidimensional evaluation of health benefits, leaving notable gaps in dormitory design. Under the Healthy China Initiative, the indoor wind environment in university dormitories directly impacts students’ health and learning efficiency. This study selects dormitory buildings in Xuzhou as the research object and employs ANSYS FLUENT 2020 software for computational fluid dynamics (CFD) simulations, combined with orthogonal experimental design methods, to systematically investigate and optimize the indoor wind environment with a focus on healthy ventilation standards. The evaluation focused on three key metrics—comfortable wind speed ratio, air age, and CO₂ concentration—considering the effects of building orientation, corridor width, and window geometry, and identifying the optimal parameter combination. After optimization based on the orthogonal experimental design, the proportion of comfortable wind speed zones increased to 44.6%, the mean air age decreased to 258 s, and CO₂ concentration stabilized at 613 ppm. These results demonstrate that the proposed optimization framework can effectively enhance indoor air renewal and pollutant removal, thereby improving both air quality and the health-related performance of dormitory spaces. The novelty of this study lies in integrating regional climate conditions with a coordinated CFD–orthogonal design approach. This enables precise optimization of dormitory ventilation performance and provides locally tailored, actionable evidence for advancing healthy campus design.

Underground supermarkets are characterized by high occupant density, long operating hours, and substantial internal loads, creating competing demands for energy efficiency, thermal comfort, and carbon reduction. 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. External-wall insulation thickness had the strongest influence on EUI and DTR, whereas roof insulation thickness most strongly affected carbon performance because of the trade-off between operational energy savings and material-related emissions. These results demonstrate the need for coordinated envelope and operational strategies when balancing energy, comfort, and carbon performance in underground commercial buildings.

To address the trade-offs among energy savings, thermal comfort, and retrofit cost in existing multi-story residential buildings in cold climate regions, this study developed a retrofit optimization framework integrating building performance simulation, artificial neural network (ANN) surrogate models, and multi-objective optimization. A typical six-story residential building in Suzhou, Anhui Province, was selected as the case building. The DesignBuilder model was calibrated against one month of measured summer data, and the design variables were screened using Morris sensitivity analysis. Latin hypercube sampling was then employed to generate 5000 samples for training separate surrogate models of annual net electricity-use intensity (ANEUI), annual discomfort hours (DH), and initial retrofit cost per unit floor area (IRC). The optimization performance of NSGA-III, MOPSO, and NSGA-III-PSO was compared, and a compromise solution was identified using a combined subjective-objective weighting TOPSIS method. NSGA-III-PSO showed a better balance between convergence and objective-space coverage. EnergyPlus re-simulation indicated that the compromise solution reduced ANEUI from 60.29 to 35.98 kWh/(m²·a) and DH from 5413.2 to 4012.5 h, corresponding to reductions of 40.3% and 25.8%, respectively, while the IRC was 398.9 CNY/m². The proposed framework provides quantitative decision support for integrated residential retrofits under comparable climatic conditions.