The variability of renewable energy sources, such as wind power, presents a significant challenge for grid operators in maintaining operational stability. This is specially true to the medium-term (from hours to days ahead), which is both influenced by recent past data and broader trends and heavily influences decision making. This research proposes a Convolutional Generator Network conditioned on the previous step of u- (latitudinal) and v- (longitudinal) wind speed components to generate wind speed scenarios using the Conditional Generative Adversarial Networks training algorithm. The model is compared to the state of the art in weather forecasting, Numerical Weather Prediction Systems. The proposed generator model outperforms the benchmark for a forth of the months in the test dataset when predicting over two weeks (28 12-hourly steps) starting from a single data point with much lower computational cost, less input data and similar long-term stability. Additionally, its forecasts are statistically equal to the state-of-the-art in 71.97 percent of series.