Wind Speed Forecast using LSTM and Bi-LSTM Algorithms over Gabal El-Zayt Wind Farm

The accurate forecast of wind speed is critical in the integration of renewable energy within the main electrical grid and an important factor for power electrical grid stability, scheduling, and planning. In this paper, we present the deep learning algorithms, Long Short-Term Memory (LSTM), and bidirectional LSTM algorithms (Bi-LSTM) using different configurations and different activation functions to evaluate the experiments and predict the provisional trend of wind speed. We used both models to predict the wind speed over Gabal Elzayt Wind Farm in Egypt. The used data-set belongs to NASA's monthly MERRA-2 wind speed datasets. The LSTM network using the “SoftSign” function as a state activation function and “Sigmoid” as a gate activation function showed better performance and the lowest RMSE error over other experiments. The trained model after validation is utilized to predict the provisional trend of wind speed for the time-frame 2020-2022 for the wind farm. LSTM and Bi-LSTM showed effectiveness to apply for the long-term wind prediction field.