Empowering sustainable design decisions: A multicriteria metric for optimizing solar protection systems

The accelerated growth of urban areas has significantly increased global energy consumption and greenhouse gas emissions, highlighting the urgency of integrating sustainable strategies into architectural design. This study proposes a methodological framework to support decision-making during the early design stages through parametric simulations and multi-objective optimization. The objective is to improve energy performance by analyzing the geometric characteristics of solar shading systems in a form of external venetian blinds across multiple design scenarios.

A statistical correlation analysis was carried out to identify the design parameters with the greatest influence on energy performance. The results indicate that the depth of the shading device and the number of louvers consistently correlate with both cooling energy demand and daylight availability, regardless of climate. These correlations align with the geometric patterns observed in the optimized solutions, underscoring the value of data-driven approaches in developing climate-responsive strategies.

The NSGA-II algorithm was used to optimize two conflicting objectives: minimizing cooling energy demand and maximizing daylight quality. The resulting Pareto front provided a diverse set of non-dominated solutions, allowing for the exploration of performance trade-offs. To enhance interpretability, K-Means clustering was applied to group optimal configurations.

The methodology was tested in two variant ASHRAE climate zones—5B (cool-dry, Mendoza, Argentina) and 5A (cool-humid, Łódź, Poland)—achieving cooling demand reductions of 39.8 % and 33.5 %, respectively, with proportional decreases in CO₂ emissions linked to the carbon intensity of the local grids, while maintaining strong performance in Useful Daylight Illuminance (UDI). As its main contribution, this study introduces the Shading Multicriteria Metric (SMM), which integrates thermal, daylight, and environmental indicators into a single adaptable metric. Beyond case-specific results, the framework demonstrates its value as a replicable methodological approach, capable of guiding early-stage design decisions toward geometrically diverse, climate-responsive, and energy-efficient solutions that align with long-term sustainability goals.