Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Download or read book Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction written by Harsh S. Dhiman and published by Academic Press. This book was released on 2020-01-21 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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