S. Marisargunam

A physics-constrained temporal modeling framework for robust short-term wind power forecasting

The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.