High computational costs hinder to assess energy behavior of whole building stocks. Artificial Neural Networks are used to perform it reliably with a novel approach. ANNs' generation is optimized by means of uncertainty and sensitivity analyses. A first family of ANNs predicts building energy consumption and thermal comfort. A second family of ANNs predicts energy retrofit scenarios.