Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Provides an up-to-date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance.

Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation.

• Features various supervised machine learning based regression models
• Offers global case studies for turbine wind farm layouts
• Includes state-of-the-art models and methodologies in wind forecasting