Loading...
Ms. C. M. Anitha is Head of the Department of Physics at JMJ College for Women, Andhra Pradesh, India. Her academic interests include Renewable Energy Forecasting, Machine Learning Applications, Sustainable Technologies, and Energy Management Systems.
Wind energy has emerged as one of the most promising renewable energy sources for achieving sustainable power generation and reducing dependence on fossil fuels. However, the intermittent and stochastic nature of wind presents significant challenges in power generation planning, grid integration, and energy management. Accurate wind energy forecasting is essential for improving power system reliability, optimizing energy production, and reducing operational uncertainties. Machine Learning (ML) techniques have demonstrated remarkable capabilities in modeling complex nonlinear relationships within meteorological and wind data, enabling highly accurate short-term, medium-term, and long-term forecasting. By leveraging algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, Gradient Boosting, and Deep Learning models, wind energy forecasting systems can significantly enhance prediction accuracy and decision-making processes. This chapter explores the principles, methodologies, applications, challenges, and future directions of machine learning-based wind energy forecasting systems, highlighting their contribution to intelligent and sustainable energy management.