Given the fact that renewable energy sources are increasing their share in the electricity market, in order to maintain the stable grid, i.e. match the production and the demand, it is crucial to have an accurate prediction of the expected accessible energy. Therefore, this paper is focused on providing the model for wind turbine production short-term forecast. The model is a deep neural network that includes LSTM, convolutional and dense layers, trained by the real-world data obtained from the wind farm in Krnovo, Montenegro. The model was successful in the goal of providing competent prediction, so performances and results of the proposed model are given in this paper.