Great importance is posed today on the refurbishment of residential buildings. However, the common practice is to compute the performances of the refurbished building referring to the actual climatic data, without considering its possible future evolutions, possibly leading to performances that greatly differ from the design phase ones. This paper investigates the effects of data uncertainty in an optimization process applied to the refurbishment practice of a residential building located in Italy, focusing on the effects produced by climate change and by the evolution of the economic situation through the research of robust solutions. The results highlight very different behaviors of both economic and energetic performances of the refurbished building in relation to the different climatic datasets used.
