Limit search to available items
Book Cover
Author Ding, Yu (Electrical and Computer Engineer), author

Title Data science for wind energy / Yu Ding
Published Boca Raton : CRC Press, [2020]
Online access available from:
ProQuest Ebook Central    View Resource Record  
ProQuest Ebook Central Subscription    View Resource Record  


Description 1 online resource : illustrations
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
Bibliography Includes bibliographical references and index
Notes Yu Ding is the Mike and Sugar Barnes Professor of Industrial and Systems Engineering and Professor of Electrical and Computer Engineering at Texas A&M University, and a Fellow of the Institute of Industrial & Systems Engineers and the American Society of Mechanical Engineers
Description based on online resource; title from digital title page (viewed on July 02, 2019)
Subject Wind power -- Data processing
Wind power -- Mathematical models
COMPUTERS / Computer Graphics / Game Programming & Design
Wind power -- Data processing.
Wind power -- Mathematical models.
Form Electronic book
ISBN 0429490976 electronic book
0429956509 electronic book
0429956517 electronic book
9780429490972 electronic book
9780429956508 electronic book
9780429956515 electronic book
electronic book
electronic book