Wind Speed Prediction Using a SEEMD-LSTM Model

Although wind energy appears to be a very promising replacement for fossil fuel energy, it faces some challenges during implementation. Producing precise predictions for wind energy is of utmost significance in energy strategic planning. This is because wind data is non-stationary and non-linear in nature; therefore, common statistical and computational methods cannot produce adequate predictions for wind. In this study, we utilized a hybrid method that combines a variation of Empirical Mode Decomposition, Smoothing Ensemble Empirical Mode Decomposition (SEEMD), and long-short-term memory neural networks (LSTM) to predict wind speed. The results of this study provide a promising forecasting method for predicting non-stationary and non-linear phenomena such as wind. The hybrid method proposed in this study successfully predicted the wind-speed data with an R2 value of 0.9353 a mean absolute error (MAE) of 0.0504 as well as the wind-direction data with an R2 value of 0.9711 and a mean absolute error (MAE) of 0.1626.