This paper investigates the effect of climatic data on the results in an optimization loop applied to a refurbished social housing building. The considered energy reduction measures involve the application of internal insulation layers to the original walls and the replacement of existing windows with more efficient ones. The optimization relies on the numerical results obtained by the underlying simulation engine, but the results can be heavily influenced by the adopted climatic data. In this study, the impact of the choice of three different TRYs (from the same area of Trieste), applied to multi-objective optimization is investigated. EnergyPlus performs the energy simulation while modeFRONTIER, with python scripting, drives the optimization. The results highlight the differences in the optimal choices a designer can obtain by changing the climatic database
