Data Science for Wind Energy

1st Edition

Yu Ding

Chapman and Hall/CRC
June 6, 2019 Forthcoming
Reference - 388 Pages - 103 B/W Illustrations
ISBN 9781138590526 - CAT# K386763

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This book provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, from near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods will be covered, including time series models, spatio-temporal model and analysis, kernel regression, decision trees, k-NN, splines, Bayesian inference, and MCMC sampling. More importantly, the data science methods will be described in the context of wind energy applications, with specific wind energy examples and case studies.


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