Abstract

This review examines the advancements and methodologies in long-term wind speed and power forecasting. It emphasizes the importance of these techniques in the integration of wind energy into power systems. The study covers a range of forecasting time horizons from monthly to multiyear forecasts. It highlights the diversity of applications and methodologies necessary to address the inherent variability and unpredictability of wind energy. Various forecasting methods including statistical models, machine learning techniques, and hybrid models are discussed in detail. The discussions demonstrate their effectiveness in improving the forecast accuracy and reliability across different temporal and geographical scales. The review identifies significant challenges such as model complexity, data limitations, and the need for forecasts to accommodate regional variabilities. The future directions include enhancing model integration, using higher-resolution data, and increasing collaborative efforts across fields to refine the forecasting methodologies. This comprehensive analysis aims to advance the knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals.