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

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

Harsh S. Dhiman is a research scholar in Department of Electrical Engineering from Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad, India. He obtained his Master's degree in Electrical Power Engineering from Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, India in 2016 and B. Tech in Electrical Engineering from Institute of Technology, Nirma University, Ahmedabad, India in 2014. His current research interests include Hybrid operation of wind farms, Hybrid wind forecasting techniques and Wake management in wind farms.

1. Introduction 2. Wind Energy Fundamentals 3. Paradigms in Wind Forecasting 4. Supervised Machine Learning Models based on Support Vector Regression 5. Decision tree ensemble-based Regression Models 6. Hybrid Machine Intelligent Wind Speed Forecasting Models 7. Ramp Prediction in Wind Farms 8. Supervised Learning for Forecasting in presence of Wind Wakes A. Introduction to R for Machine Learning Regression A.1 Data handling in R A.2 Linear Regression Analysis in R A.3 Support vector regression in R A.4 Random Forest Regression in R A.5 Gradient boosted machines in R