Short-Term Wind Power Forecasting Using Transformer-XGBoost Hybrid Model
Abstract
Accurate wind forecasting is critical to ensure stable and efficient integration of renewable energy resources in modern power systems. However, the inherent variability and non-stationarity of wind pose a significant forecasting problem for modern power system operators to ensure power system stability. A new hybrid forecasting model incorporating a Transformer Encoder and XGBoost regressor is proposed in this paper to enhance one-hour-ahead short-term forecasting of wind power generation. The model employs Transformer Encoders to exploit complex temporal relationships from sequences of 24-hour past wind data, learning daily and periodic trends, ramps, and hidden dynamics. The generated temporal feature vectors are then combined with meteorological variables such as wind speed and direction at various altitudes and passed on to the XGBoost model to obtain regression forecasts. The proposed approach has been validated on real-world datasets obtained from commercial wind energy farms. Experimental results demonstrate that the hybrid model outperforms standalone Transformer and XGBoost models by achieving accurate values for mean squared error, mean absolute error and coefficient of determination. In addition, model has tested for long-term forecasting as well. Although the model is effective for short-term forecasting, due to increased uncertainty and error propagation, its performance degrades with extended forecasting horizons.