Multi-Objective Optimization of Energy Performance, Winter Cold Discomfort, and Daylighting in a Contemporary Yi Rural House Prototype: A Case Study in Xichang, China

Policy-led housing renewal and standardized rural-house atlases are reshaping contemporary Yi rural housing, yet trade-offs among energy performance, winter cold discomfort, and daylighting remain poorly quantified. This study aimed to quantify the effects of design parameters on energy use, winter cold discomfort, and daylight availability, and to examine the trade-offs among these objectives in a contemporary single-storey three-bay Yi rural house prototype in Xichang. The parametric prototype was informed by field surveys, policy documents, and a local rural-house atlas. Building performance simulations generated 1667 valid cases for PMV-based cold discomfort intensity (PMV-CDI), useful daylight illuminance (UDI300–2000), and simulation-based conditioned-zone energy use intensity (EUI). Six machine-learning algorithms were compared to develop surrogate models, with all selected models achieving independent-test CV(RMSE) values below 2%. SHAP analysis identified distinct contributions of envelope, window, shading, and spatial features across the three objectives. Surrogate-assisted NSGA-III generated 388 empirical nondominated solutions forming a continuous approximate Pareto front. The Pareto front revealed diminishing daylight gains accompanied by increasingly pronounced winter cold discomfort toward the high-daylight region. Hierarchical clustering translated the continuous Pareto front into three interpretable performance-priority scenarios, enabling comparison of the objective trade-offs and associated parameter combinations. The resulting framework provides a quantitative basis for comparing performance priorities in standardized rural-house design while retaining locally defined spatial and construction constraints.