Artículos relacionados a Data Science for Wind Energy

Data Science for Wind Energy - Tapa blanda

Ding, Yu

 
9780367729097: Data Science for Wind Energy
Tapa blanda
ISBN 10: 0367729091 ISBN 13: 9780367729097
Editorial: Chapman and Hall/CRC, 2020

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

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.

De la contraportada

This book shows how data science methods can improve decision making for wind energy applications. A broad set of data science methods will be covered, and the data science methods will be described in the context of wind energy applications, with specific wind energy examples and case studies.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Chapman and Hall/CRC
Año de publicación
2020
Idioma
Inglés
ISBN 10 
0367729091
ISBN 13 
9780367729097
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
424
Contacto del fabricante
Taylor and Francis, GPSR Taylor and Francis
gpsr@taylorandfrancis.com

Taylor & Francis Verlag GmbH, Kaufingerstraße 24
München
80331
Alemania