Artificial Intelligence Application for Wind Power Forecasting for Wind Turbine Generators: A Case Study in Vietnam

Abstract

Accurate short - term wind power forecasting plays a critical role in enhancing the reliability and efficiency of wind farm operations, especially in regions with high wind variability such as coastal Vietnam. This study investigates the application of three advanced artificial intelligence (AI) Long short - term memory (LSTM), Gated recurrent unit (GRU), and Xtr eme gradient boosting (XGBoost) for wind power prediction using real - world SCADA data collected from the WT01 turbine - 4MW at the wind farm Ninh Thuan over a 360 - day period. The forecasting performance of these models is evaluated under three feature scenarios: (i) wind speed only, (ii) wind speed combined with rotor speed, and (iii) a comprehensive set of five features including wind speed, rotor speed , pitch angle, vibration level, and internal temperature. Model performance is assessed using standard metrics such as RMSE, NMAPE, and training time. Results show that LSTM achieved the highest accuracy, with RMSE and NMAPE of 654.15 kW and 12.82%, respec tively, when trained with all five features. GRU delivered comparable results with shorter training time, while XGBoost exhibited superior computational efficiency but slightly lower accuracy. These findings highlight the importance of feature richness in enhancing prediction accuracy and suggest that the choice of forecasting model should consider both accuracy requirements and computational constraints. The study offers practical insights for AI - based forecasting system design tailored to the operational needs of wind power facilities in Vietnam and similar regions .