Orientation-Specific Multi-Objective Daylight Optimization of Office Buildings: Integrating Window Geometry and External Shading

This study addresses the multi-objective trade-off between daylight utilization and reductions in the annual spatial-mean DGP indicator in office spaces in cold regions. A parametric daylight optimization framework was developed for a typical side-lit office in Lanzhou, using window-to-wall ratio (WWR), window aspect ratio (AR), external shading depth (SD), and building orientation (ORI) as design variables, and useful daylight illuminance (UDI), spatially averaged point-based daylight autonomy (mean DA), and annual spatial-mean daylight glare probability (DGP) as performance metrics. First, 500 baseline full-factorial simulations were conducted, and multifactor analysis of variance was used to quantify the relative contributions of the design variables and their two-factor interactions to the modeled response variation within the predefined full-factorial design domain. Second-order response surface surrogate models were then established separately for four orientations and validated using 48 independent off-grid samples. NSGA-II was subsequently employed to identify three-objective Pareto fronts, while TOPSIS was used to select representative solutions under different performance preferences. Within the investigated baseline full-factorial design domain, WWR showed the largest relative contribution to the model-explained variation in UDI and mean DA, accounting for 69.48% and 58.26%, respectively, whereas ORI showed the largest relative contribution to the annual spatial-mean DGP response, accounting for 56.20%. High-ranking Pareto solutions within the baseline design domain cluster near the upper bounds of WWR and AR, indicating that fixed rectangular bounds may truncate potentially high-performing regions. To examine this boundary effect, 48 geometry-constrained extended-domain training samples and 24 independent validation samples were introduced, with optimization restricted to the actual training-data coverage and a 0.30 m window-edge geometric constraint. Within the investigated simulation-supported extended domain, the maximum UDI and mean DA were higher than their baseline-domain extrema by approximately 2.4–3.9 and 7.1–8.6 percentage points, respectively, while the minimum values of the annual spatial-mean DGP indicator were approximately 32–47% lower under the specified simulation conditions. To further assess optimization reliability, 20 additional Pareto candidates were re-simulated, showing within-orientation ranking agreement of 92.5% for UDI and 100% for both mean DA and the annual spatial-mean DGP indicator. Distinct window–shading trade-offs were observed among the objectives: higher mean DA favored larger transparent openings with shallow shading, lower annual spatial-mean DGP values favored smaller WWRs with deeper shading, whereas higher UDI depended more strongly on a compensatory coupling between larger windows and stronger shading. Overall, the proposed framework reveals orientation-dependent performance trade-offs across a broader design space while controlling surrogate-model extrapolation risk, providing a quantitative basis for early-stage parametric fenestration design in office buildings.