Data-Driven Multi-Objective Optimization of Building Envelope Retrofits for Senior Apartments in Beijing

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R2 values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings.