A review of short-term wind power generation forecasting methods in recent technological trends - SAĞLAM, ŞAFAK; ORAL, BÜLENT (My University, 2024)
Climate change and the escalating demand for energy are among the most pressing global challenges of our era. Renewable energy sources, such as wind energy, are considered a viable solution to these issues. However, the integration of renewable energy sources into electric power systems also presents operational challenges, particularly in terms of uncertainty. In order to mitigate this uncertainty, it is crucial to improve the accuracy of generation forecasting methods for wind energy. This review explores various wind power forecasting methods, categorizing them by factors such as time frame, and model structure. Special attention is given to short-term forecasting, crucial for the day-ahead electricity market. This study traces the evolution of wind power forecasting, from early statistical approaches to the integration of numerical weather prediction, machine learning, neural networks, and advanced techniques. Its aim is to provide valuable insights into wind power forecasting methods for stakeholders, including grid operators, traders, and wind farm operators. This review serves as a vital resource for researchers and industry professionals navigating the dynamic field of wind power forecasting, contributing to effective renewable energy resource management in a rapidly evolving energy sector.
Keywords: Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Mühendislik ve Teknoloji; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Mühendislik; ENERJİ VE YAKITLAR; Engineering, Computing & Technology (ENG); ENGINEERING; ENERGY & FUELS; Genel Enerji; Fizik Bilimleri; General Energy; Physical Sciences; Artificial Neural Networks; Deep Learning; Machine Learning; Numerical Weather Prediction; Short Term; Support Vector Machine; Wind Energy Forecasting