Optimizing Wind Speed Prediction: A Critical Comparison of Advanced Neural Network Architectures

Abstract

Since wind speed affects grid stability, wind farm efficiency, and anticipated energy output, accurate wind speed forecasting is essential for wind power generation success. Predicting wind speed is difficult, though. Numerous geographic and meteorological factors impact the complex, nonlinear, and constantly evolving process. Conventional models frequently ignore these dynamic patterns, which causes uncertainty in energy predictions. We created a hybrid model for short-term wind speed forecasting that combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks in order to address this issue. LSTM excels at identifying long-term trends, whereas GRU excels at managing short-term fluctuations. We also incorporate important environmental factors like temperature, pressure, humidity, and wind direction to enhance the approach's performance. Using common metrics like RMSE, MAE, and R2, we compared this hybrid model to standalone LSTM and GRU models. The hybrid model outperformed the others, achieving an R2 of 0.998 and an RMSE of just 0.230, according to the encouraging results. For regions like Dakhla, Morocco, where precise wind speed forecasts are essential for the development and expansion of renewable energy projects, this methodology is especially advantageous. We can maximize energy production, lessen our reliance on fossil fuels, and hasten the transition to a more sustainable and independent energy future with better forecasts.