Energy consumption is growing extensively, which is caused by new demanding technological applications and continuously changing lifestyles, also with respect to climate change. Climate change is a significant issue and scientific reports notice the temperature environment continuously increasing, particularly in the summer. To alleviate the heat, people in many countries tend to use air conditioning systems in residential and business buildings. This puts additional pressure on the electricity network and the energy producers must be able to predict such events. It is agreed worldwide that harvesting renewable energy is the best option for fighting climate change. For example, recently, the number of electric cars has increased and it becomes more and more attractive to utilize green energy, e.g., produced by wind turbines, for them. The advantages of wind energy have intensively been studied, and a wide range of methods to create very short-term, short-term, medium-term, and long-term predictions using wind energy models or wind speed profiles are in use [1,2]. However, some of the forecasting methods are highly complex and costly in computing [3,4]. This study uses a gated recurrent unit (GRU) model, a deep learning model, to efficiently perform medium-term predictions of wind energy production. There is effort to apply these medium-term predictions to create long-term forecasting models. The literature has reported that GRUs are faster than long short-term memory (LSTM) models, which have been used in recent studies, can deal with relatively fewer data, and are cheaper in computing. The study applies empirical wind speed data from 5 years, which the Iceland Metrological office has measured at 10 m height at the Búfrell location. The log law is used to scale the speed up to 55 m, which is the height of an Enercon E44 wind turbine hub. The predictions are performed on the DAM module of the DEEP cluster at the Jülich Supercomputing Centre. The parallel machine allows to speed up the model scaling. The results show that the proposed model can predict medium and long-term wind speeds as a function of the ratio of training data. This method conducts the forecasting cheaper in computing than LSTM but with equal performance.