This article presents a review of wind power forecasting techniques using supervised machine learning algorithms, including neural networks and decision trees.
This tutorial provides a step-by-step guide to implementing supervised machine learning algorithms for wind power forecasting, including data preprocessing and model evaluation.
The National Renewable Energy Laboratory (NREL) provides an overview of wind energy forecasting using machine learning, including supervised and unsupervised techniques.
This study proposes a short-term wind power forecasting model using supervised learning algorithms, including support vector machines and random forests.
This online course covers the application of supervised machine learning algorithms to wind power forecasting, including data analysis and model deployment.
This book chapter reviews recent advances in wind power forecasting using machine learning, including supervised and deep learning techniques.
This case study applies supervised machine learning algorithms to wind power forecasting, including a comparison of different algorithms and evaluation metrics.
This video tutorial provides a step-by-step guide to implementing supervised machine learning algorithms for wind power forecasting, including data preparation and model training.