A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

Abstract

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power (WP) production. One of the major challenges in wind energy production is the instability of WP generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning (DL) are delivering competitive performance versus traditional methods, like numerical weather prediction (NWP), statistical models and machine learning (ML) models. This is truer for short-term prediction. Since there is a relationship between methods, climates and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper presents a novel model that combines the variational mode decomposition (VMD) method with a long short-term memory (LSTM) model for next-hour wind speed (WS) prediction in a hot desert climate, such as the climate in Saudi Arabia. The proposed model performance is compared to two other hybrid models, six DL models and four ML models using different feature sets. Also, the proposed model is tested on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill (FS) between 61% and 74% based on mean absolute error (MAE), 64% and 72% based on root mean square error (RMSE), and 59% and 68% based on mean absolute percentage error (MAPE) for locations in Saudi Arabia.