The environmental constraints related to the reduction of emissions and to the contrast to climate change imply an increasing penetration of renewable energy generation. Since renewable energy sources like solar and wind are not programmable and often unpredictable, the grid balancing is becoming more and more challenging. In this scenario, buildings energy flexibility could play a key role through demand side management/load control and demand response actions. The study presented in this thesis work is related to the application of machine learning techniques to the field of building HVAC systems control, in order to enhance their flexibility. The first part of the thesis is related to the study of black box modeling of building thermodynamic behaviour through artificial neural networks.