Bi-objective optimization of fenestration using an evolutionary algorithm approach

This study assesses the trade-offs between the conflicting objectives of reducing heating intensity and increasing daylight utilization in the context of Swedish residential spaces, specifically for a north oriented bedroom. The optimization process is conducted within the visual programming environment of Grasshopper, where the simulation engines of Energyplus, Radiance and Daysim are interconnected and combined with the Strength Pareto Evolutionary Algorithm 2 (SPEA2). A fenestration algorithm is proposed that generates conventional window geometries in differing size and placement while considering the view towards the exterior environment. Iterations are assessed for their influence on annual measures of heating energy intensity, daylight illuminance deficit (ADID), electrical lighting use. Results indicated that diverse and efficient solutions can be generated by this method, allowing the design team to select among them based on higher-level / unquantifiable information. It was proven that the commonly used WWR parameter is not sufficient to assess the thermal and luminous needs of space. Different window configurations can yield different results depending on the actual position of the opening.