This study develops an interpretable machine learning (ML) surrogate to predict hourly indoor air temperature and discomfort indicators for a representative Mexican social-housing prototype in San Luis Potosí (cold semi-arid, Köppen–Geiger BSk). A four-zone EnergyPlus model with constant window opening (50%) and no internal gains was used to generate a parametric dataset spanning 24 building orientations, seven roof solar absorptance levels, and two neighborhood configurations (surrounded vs. corner). Zone-specific bagged-tree regression models were trained in MATLAB using weather predictors, temporal indicators, and weather-memory features (including outdoor temperature lags and rolling averages). Orientation and roof absorptance were included as explicit design predictors, enabling the surrogate model to generalize across the full combinatorial design space rather than requiring a separate model for each configuration. Interpretability was assessed with SHAP values. Evaluated on orientation–absorptance combinations deliberately held out during training, the surrogate achieved high accuracy across zones of the house (R2 = 0.98–0.99; RMSE = 0.31–0.67 °C) with stable, near-zero-centered residuals. When propagated into adaptive-comfort metrics computed directly relative to the monthly neutral temperature Tn, ML predictions preserved the main cold and hot discomfort degree-hour patterns across the full design space. The proposed surrogate enables rapid, physically consistent comfort-oriented screening of roof finishes and orientation choices in naturally ventilated social housing.
Jiménez et al. (Sat,) studied this question.