Wind energy is an emission free low-carbon energy source that has employed great advances in technology in recent years. The unpredictable and random characteristics of wind, including variables like wind speed, direction, barometric pressure and temperature present distinct challenges for precise wind power prediction. Accurate forecasts of wind power generation are crucial for the effective management of energy grids, ensuring stability, and the seamless integration of wind energy into existing power infrastructures. This study introduces a novel, Transformer-based, Dynamic context-aware power forecasting model tailored to enhance the accuracy of wind power forecasting. This model adapts the Transformer architecture, incorporating innovative modifications to tackle the complications of wind power forecasting. It improves the extraction of contextual features while simplifying the model’s structure. The results demonstrate the Normalized values of Root mean square error (NRMSE) 1.603, 2.219, 2.76 and 2.69 for Dynamic context-aware, Transformer, LSTM and GRU model respectively. Dynamic context-aware model outperforms other models achieving the lowest error rates in accuracy for prediction to provide comprehensive knowledge to decision-makers interested in wind turbines and energy optimization.
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