Abstract The contribution of wind power systems to the reduction of the impact of fossil fuels sources has increased more and more during the last decades leading to a greater attention to the estimation of the performances of renewable power plants. However, forecast methods of productivity of onshore/offshore wind farms still suffer, in terms of accuracy, the innate variability of the energy resources and the effect of components failures. This paper proposes a novel “hybrid” approach for the estimation of the energy conversion of onshore wind farms. The model combines the Jensen wake mathematical theory with a stochastic dependability model, a Fault Tree, to better forecast the energy production. A new key index was conceived to optimize the preventive maintenance of wind turbines. This model was tested on a real case study, a wind farm (25.5 MWp) located in the south of Italy. Results were promising because the model achieved a twofold objective to improve the accuracy of the energy conversion forecast and to provide a support decision system for the activities of maintenance planning.