Wind speed forecasting one step ahead using different input vectors in a neural network

This study introduces a method for predicting wind speed in Baja California Sur, Mexico, using long-short-term memory (LSTM) neural networks applied to hourly time series data. Measurements from January 2016, provided by Mexico's Federal Electricity Commission (CFE), were analyzed to optimize the model inputs and reduce forecast errors. The data was statistically described to identify relevant patterns and then normalized to improve model training. Several LSTM network configurations were tested and compared based on error metrics. Initial models with six variables in input vectors were unsatisfactory. However, three additional models with five, three, and two inputs were developed. The model with five inputs and one output (5I, 1O) achieved the lowest error metrics: Mean Absolute Error (MAE) of 0.72, Root Mean Square Error (RMSE) of 0.923, and Normalized Root Mean Square Error (NRMSE) of 0.161, indicating the best performance among the tested models.