Currently there is no global approach to model then characterize solar thermal systems for building application. Results of the existing approaches are valid only for specific conditions (climate and thermal building properties). In a previous study a generic methodology to model solar combisystems (SCS) was developed. This methodology was applied to predict the energy consumption of two kinds of systems: SCS combined with a gas boiler and SCS combined with a heat pump. In the current paper, an extension of the methodology to SCS combined with an absorption chiller is presented. The methodology is based on the development of an artificial neural network (ANN) that models the dynamic of the system. The developed neural models were able to predict, with a good precision degree, the annual energy performance of the system in different climates based on a learning sequence of only 12 days. The satisfactory results emphasize the generic character of the methodology and show that it could be used, in the future, as an energy performance evaluation tool. Also, the proposed approach will be helpful in the context of energy performance guarantees.