A Data-Driven Multi-Objective Optimization Approach for Improving Energy and Comfort Performance in Residential Buildings

Examining energy-related issues and comfort conditions in residential buildings is essential for achieving sustainable development. Accordingly, this study aims to optimize building envelope design parameters—including air-tightness values derived from empirical measurements—to strike a balance between energy consumption, thermal comfort, and visual comfort. A multi-stage, systematic methodology is proposed, featuring a hybrid artificial intelligence-based framework for prediction and optimization, grounded in building performance simulations. A BlowerDoor test was conducted on the residential building under study, and the results were integrated into a building performance model. Data generated through these simulations were used to compare the performance of various machine learning meta-models, including Artificial Neural Networks, Support Vector Regression (SVR), and XGBoost. The SVR model, which demonstrated the lowest error rate and highest reliability across most objectives, was selected as the meta-model for the optimization phase. This model served as the objective function for a multi-objective optimization process carried out using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The results of this surrogate-model-based optimization indicate a 21.69% reduction in energy consumption, alongside improvements of 17.2% in visual comfort and 20% in thermal comfort compared to the baseline state. Furthermore, the analysis reveals that air-tightness influences daylighting performance through its interaction with other parameters, such as the Window-to-Wall Ratio (WWR) and building orientation. Finally, the SHapley Additive exPlanations (SHAP) method was employed to determine the relative importance of the design parameters following the multi-objective optimization. The analysis results indicate that the order of influence of various parameters differs depending on the specific objective. While the window-to-wall ratio (WWR) and orientation are the primary factors for enhancing visual comfort, factors such as orientation, infiltration, and the thermal transmittance of building components are the most significant for improving energy efficiency and thermal comfort. Consequently, the integration of data-driven approaches—based on surrogate models—into building performance assessment can contribute significantly to the sustainability of residential buildings and offer a new research framework for future studies.