Books in the Wind Energy Engineering series

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 modelsOffers global case studies for turbine wind farm layoutsIncludes state-of-the-art models and methodologies in wind forecasting

Fundamentals of Wind Farm Aerodynamic Layout Design, Volume Four provides readers with effective wind farm design and layout guidance through algorithm optimization, going beyond other references and general approaches in literature. Focusing on interactions of wake models, designers can combine numerical schemes presented in this book which also considers wake models¿ effects and problems on layout optimization in order to simulate and enhance wind farm designs. Covering the aerodynamic modeling and simulation of wind farms, the book's authors include experimental tests supporting modeling simulations and tutorials on the simulation of wind turbines. In addition, the book includes a CFD technique designed to be more computationally efficient than currently available techniques, making this book ideal for industrial engineers in the wind industry who need to produce an accurate simulation within limited timeframes.