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Data Science for Wind Energy - Ding, Yu
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Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, 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 covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance ...

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Data Science for Wind Energy 2020, Chapman & Hall/CRC

ISBN-13: 9780367729097

Paperback

Data Science for Wind Energy 2019, CRC Press, London

ISBN-13: 9781138590526

Hardcover