Proposal of a new automated workflow for the computational performance-driven design optimization of building energy need and construction cost

Delaying the analysis of the building performance to the final stages of the design process can often cause the actual results to differ from the expected ones, with the issue of significant redesign costs. The aim of this work is to improve the designer's control on thermal and geometric variables of the building in the first stages of the design process, to obtain an optimized building from different points of view. An innovative computational performance-driven design optimization workflow is proposed. The integration of a parametric algorithmic modelling tool in a genetic algorithm powered performance optimization procedure allows to minimize energy need and construction cost of the building, and to optimize the natural illumination levels, affecting several geometric variables. This approach is tested on the early design stages for the new construction of an educational building located in the centre of Italy (L'Aquila). The results are post-processed in order to provide an innovative graphical visualization of the outputs to improve the decision-making process. Useful insights on the influence of different design choices on three key performance indicators can be drawn for the case study building, by comparison of the Pareto solutions: optimization results highlight the strongest influence of window-to-wall ratio, among the other variables, on energy need and useful daylight illuminance.