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

This well researched volume tells the story of music education in Japan and of the wind band contest organized by the All-Japan Band Association. Identified here for the first time as the world’s largest musical competition, it attracts 14,000 bands and well over 500,000 competitors. The book’s insightful contribution to our understanding of both music and education chronicles music learning in Japanese schools and communities. It examines the contest from a range of perspectives, including those of policy makers, adjudicators, conductors and young musicians. The book is an illuminating window on the world of Japanese wind bands, a unique hybrid tradition that comingles contemporary western idioms with traditional Japanese influences. In addition to its social history of Japanese school music programs, it shows how participation in Japanese school bands contributes to students’ sense of identity, and sheds new light on the process of learning to play European orchestral instruments.