This article presents a review of machine learning algorithms for wind power 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 model selection and hyperparameter tuning.
This research paper from MIT explores the application of deep learning algorithms, such as convolutional neural networks and recurrent neural networks, for wind power forecasting.
This online course on Coursera provides an introduction to wind power forecasting using machine learning algorithms, including data preprocessing, feature engineering, and model evaluation.
This article presents a study on the application of ensemble machine learning algorithms for wind energy forecasting, including bagging, boosting, and stacking.
This conference paper explores the integration of machine learning algorithms with IoT sensors for wind power forecasting, including data collection, processing, and visualization.
This news article discusses the application of machine learning algorithms for renewable energy forecasting, including wind, solar, and hydro power, and their potential to improve grid stability.
This Kaggle competition provides a dataset and a tutorial on using gradient boosting machines for wind power forecasting, including data exploration, feature engineering, and model tuning.