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books
| book details |
Data Science for Wind Energy
By (author) Yu Ding
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| on special |
normal price: R 2 168.95
Price: R 2 059.95
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| book description |
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 sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons (CC) 4.0 license.
| product details |
Normally shipped |
Publisher | Taylor & Francis Ltd
Published date | 18 Dec 2020
Language |
Format | Paperback / softback
Pages | 424
Dimensions | 234 x 156 x 0mm (L x W x H)
Weight | 620g
ISBN | 978-0-3677-2909-7
Readership Age |
BISAC | business & economics / statistics
| other options |
Normally shipped |
Readership Age |
Normal Price | R 2 576.95
Price | R 2 447.95
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Mason Coile
Paperback / softback
224 pages
was: R 520.95
now: R 468.95
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A terrifying locked-room mystery set in a remote outpost on Mars.
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An epic love story with the pulse of a thriller that asks: what would you risk for a second chance at first love?
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