This article presents a review of machine learning algorithms for wind speed forecasting, including neural networks, decision trees, and support vector machines.
The National Renewable Energy Laboratory (NREL) provides an overview of machine learning techniques for wind energy forecasting, including wind speed and direction prediction.
This article explores the use of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks for wind speed forecasting, with example code in Python.
This study evaluates the performance of different machine learning algorithms, including random forest, gradient boosting, and neural networks, for wind speed forecasting.
This online tool uses machine learning algorithms to forecast wind speed and direction, providing users with accurate predictions for wind energy applications.
This online course provides an introduction to machine learning techniques for wind speed forecasting, covering topics such as data preprocessing, model selection, and evaluation.
This research project at MIT explores the application of machine learning algorithms for renewable energy forecasting, including wind speed and solar irradiance prediction.
This review article provides an overview of machine learning algorithms for wind speed forecasting, including their strengths, weaknesses, and applications in the wind energy industry.