Building Optimization through a Parametric Design Platform:Using Sensitivity Analysis to Improve a Radial-BasedAlgorithm Performance

Performance-based design using computational and parametric optimization is an effectivestrategy to solve the multiobjective problems typical of building design. In this sense, this studyinvestigates the developing process of parametric modeling and optimization of a naturally ventilatedhouse located in a region with well-defined seasons. Its purpose is to improve its thermal comfortduring the cooling period by maximizing Natural Ventilation Effectiveness (NVE) and diminishingannual building energy demand, namely Total Cooling Loads (TCL) and Total Heating Loads (THL).Following a structured workflow, divided into (i) model setting, (ii) Sensitivity Analyses (SA), and(iii) Multiobjective Optimization (MOO), the process is straightforwardly implemented througha 3D parametric modeling platform. After building set up, the input variables number is firstlyreduced with SA, and the last step runs with an innovative model-based optimization algorithm(RBFOpt), particularly appropriate for time-intensive performance simulations. The impact of designvariables on the three-performance metrics is comprehensively discussed, with a direct relationshipbetween NVE and TCL. MOO results indicate a great potential for natural ventilation and heatingenergy savings for the residential building set as a reference, showing an improvement between14–87% and 26–34% for NVE and THL, respectively. The approach meets the current environmentaldemands related to reducing energy consumption and CO2emissions, which include passive designimplementations, such as natural or hybrid ventilation. Moreover, the design solutions and buildingorientation, window-to-wall ratio, and envelope properties could be used as guidance in similartypologies and climates. Finally, the adopted framework configures a practical and replicableapproach for studies aiming to develop high-performance buildings through MOO.