Wind Speed Forecasting: A Comparative Study of Decomposition Techniques

Renewable energy is sourced from natural resources that are continually available. Wind energy is a main category of renewable energy, which largely depends on wind speed. Accurate wind speed forecasting is essential for incorporating renewable energy into the electrical grid. Moreover, it is crucial to ensure the safety of wind turbines by anticipating extreme weather conditions and implementing necessary precautions. Predicting wind speed presents several challenges because of dynamic and complex nature of atmospheric conditions. Traditional methods for wind speed forecasting, such as statistical models and basic physical approaches, often face limitations in accuracy, flexibility, and handling of complex data patterns. Decomposition methods have proven to be an effective tool for enhancing wind speed forecasting accuracy. This work presents a comparative analysis of various decomposition techniques with the help of Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and bidirectional LSTM. The study will examine each method's advantages and disadvantages about wind speed forecasts. Key factors like handling non-stationarity, capturing different wind speed components (trend, seasonal, and high-frequency), and computational complexity will be compared. It is demonstrated that the decomposition greatly improves forecasting accuracy, with the suggested model obtaining RMSE values approximately 3% lower.