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Author Ding, Yu (Electrical and Computer Engineer), author. https://id.oclc.org/worldcat/entity/E39PCjw4hcKfbdKFvTQXrQHGf3

Title Data science for wind energy / Yu Ding.

Publication Info. Boca Raton, Florida : CRC Press, Taylor & Francis Group, CRC Press is an imprint of Taylor & Francis Group, an Informa business [2020]

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 All Libraries - Shared Downloadable Materials  Taylor & Francis Open Access Ebook    Downloadable
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 University of Saint Joseph: Pope Pius XII Library - Internet  WORLD WIDE WEB E-BOOK TAYLOR&FRANCIS    Downloadable
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Description 1 online resource
Bibliography Includes bibliographical references.
Note Description based on print version record.
Contents Introduction -- Part I: Wind field analysis: A single time series model -- Spatio temporal models -- Regime-switching methods for forecasting -- Part II: Wind turbine performance analysis: Power curve modeling and analysis -- Production efficiency analysis and power curve -- Quantification of turbine upgrade -- Wake effect analysis -- Part III: Wind turbine reliability management: Overview of wind turbine maintenance opti- mization -- Extreme load analysis -- Computer simulator-based load analysis -- Anomaly detection and fault diagnosis -- Bibliography -- Index.
Summary 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. 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
Local Note Taylor & Francis Taylor & Francis eBooks: Open Access
Subject Wind power -- Data processing.
TECHNOLOGY & ENGINEERING -- Mechanical.
BUSINESS & ECONOMICS -- Statistics.
COMPUTERS -- General.
COMPUTERS -- Computer Graphics -- Game Programming & Design.
Wind power -- Data processing
Wind power -- Mathematical models
Other Form: Print version: Data science for wind energy Boca Raton, Florida : CRC Press, [2019] 9780429490972 (DLC) 2019004826
ISBN 9780429956508 (ePub)
0429956509
9780429490972 (electronic book)
0429490976 (electronic book)
9780429956515 (electronic book)
0429956517 (electronic book)
9780429956492 (electronic book)
0429956495 (electronic book)
9781138590526
1138590525
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