Data Science for Wind Energy

Yu Ding

ISBN 10: 0367729091 / ISBN 13: 9780367729097

Published by Chapman and Hall/CRC, 2020

About this Item

Data Science for Wind Energy | Yu Ding | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367729097 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 128399309

Bibliographic Details

• Title: Data Science for Wind Energy
• Publisher: Chapman and Hall/CRC
• Publication Date: 2020
• Language: English
• ISBN 10: 0367729091
• ISBN 13: 9780367729097
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 23D millimeters 234 millimeters width by 156 millimeters height by 23 millimeters depth
• Weight: 644 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

Dr. Yu Ding is the Anderson-Interface Chair and Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech in 2023, he was the Mike and Sugar Barnes Professor of Industrial and Systems Engineering at Texas A&M University and served as Associate Director for Research Engagement of Texas A&M Institute of Data Science. Dr. Ding's research is in the area of data and quality science. He received the 2019 IISE Technical Innovation Award and 2022 INFORMS Impact Prize for his data science innovations impacting wind energy applications. Dr. Ding is a Fellow of IISE and ASME. He has served as editor or associate editor for several major engineering data science journals, including as the 14th Editor in Chief of IISE Transactions, for the term of 2021-2024.