Correcting forecast errors improves weather prediction accuracy, which is vital for optimizing decision-making in renewable energy production. This study investigates the application of Long Short-Term Memory (LSTM) models to correct 24-h wind speed forecast errors produced by the Global Forecast System (GFS), a physical meteorological model. The focus is on error correction, quantified as the difference between observed and predicted values. The dataset includes 6-hourly data over a 24-h forecasting horizon in 20 locations of the Portuguese territory. LSTM models are trained to predict forecast errors, which are subsequently used to generate a corrected time series by applying a mean bias correction to GFS forecasts and adding the LSTM-predicted error. This corrected series is then compared to the original GFS forecasts. The results show that the corrected GFS forecast yields relevant improvements in performance (RMSE, MAE and \(R^2\) ) in comparison with the GFS forecast, where the largest improvements were observed in locations where GFS traditionally underperforms, particularly in high-altitude regions. This methodology shows promise in improving wind speed forecasting accuracy for renewable energy applications in Portugal, enhancing operational efficiency and supporting sustainable energy goals.