The National Renewable Energy Laboratory (NREL) is using machine learning to improve wind power predictions, reducing uncertainty and increasing the efficiency of wind energy production.
This article presents a comprehensive review of deep learning techniques for wind power prediction, highlighting the potential of machine learning to improve the accuracy of wind energy forecasts.
This review article discusses the current state of machine learning applications in wind energy, including wind power prediction, wind turbine control, and condition monitoring, and identifies future research directions.
This online course covers the fundamentals of machine learning and data analytics for wind power prediction, including data preprocessing, feature engineering, and model evaluation.
This article discusses the potential of machine learning and artificial intelligence to improve wind power forecasts, reducing the costs and increasing the efficiency of wind energy production.
This GitHub repository provides a comparative study of LSTM and GRU models for wind power prediction, including code and data for reproducing the results.
This case study presents the application of machine learning techniques to improve wind power predictions for a wind farm, highlighting the benefits and challenges of using machine learning in wind energy production.
This video presents a webinar on the use of machine learning and numerical weather prediction for wind power forecasting, including a discussion of the challenges and opportunities in this field.