Cross-domain indoor performance prediction via in-context learning: A CFD-TabPFN-SHAP framework integrating air quality, energy, and thermal comfort

• Validated CFD dataset established for IAQ, comfort, and energy performance • Explainable TabPFN–SHAP framework proposed for multi-target indoor modeling • TabPFN outperforms BPNN, XGBoost, CatBoost with up to 95% MAE and 86% RMSE reduction • SHAP analysis reveals dominant roles of supply temperature, RH, and exhaust temperature Efficient indoor environmental management is vital for reducing energy use while safeguarding indoor air quality and occupants’ comfort. However, due to difficulty of obtaining comprehensive datasets across multiple domains, many current studies still focus on a single performance metric, with dataset limitations preventing the development of robust and accurate multi-objective predictive models and operate as opaque data-driven systems with limited explanatory capability. This work developed CFD datasets derived from an experimentally validated model to systematically generate data covering parameters of indoor air quality, energy use, and thermal sensation with varied ventilation conditions. This dataset is further used for machine learning (ML) analysis. A cutting-edge Tabular Prior-data Fitted Network (TabPFN) is adopted for multi-target prediction and benchmarked against XGBoost, CatBoost, and Backpropagation Neural Networks (BPNN). SHapley Additive exPlanations (SHAP) method was used to elucidate its predictions, identifying how the most influential parameters govern the variations in indoor environmental behavior. The findings indicate that TabPFN outperforms the other three models in both predictive accuracy and computational efficiency, with MAE reduced by 62.08–95.4% and RMSE by 49.15–85.50%, while inference is accelerated by 67.5–85.9%. SHAP analysis quantified nonlinear and directional contributions of features, linking model outputs to physical mechanisms such as draft risk, thermal sensation, and cooling load demand. The proposed TabPFN-SHAP framework is expected to offer valuable insights to guide the optimisation of building environmental control strategies.