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
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.
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
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.
Chapter 1 Introduction
Part I Wind Field Analysis
Chapter 2 A Single Time Series Model
Chapter 3 Spatiotemporal
Chapter 4 Regimeswitching
Part II Wind Turbine Performance Analysis
Chapter 5 Power Curve Modeling and Analysis
Chapter 6 Production Efficiency Analysis
Chapter 7 Quantification of Turbine Upgrade
Chapter 8 Wake Effect Analysis
Chapter 9 Overview of Turbine Maintenance Optimization
Chapter 10 Extreme Load Analysis
Chapter 11 Computer Simulator Based Load Analysis
Chapter 12 Anomaly Detection and Fault Diagnosis
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.
"This is the first book that focuses on the data science methodologies and their applications in a growing field, wind energy. It is well-organized and well-written. It will enhance the knowledge base of data science and its applications in the wind energy field."
-- Elsayed A. Elsayed, Professor, Rutgers University
Über den 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.
Inhaltsverzeichnis
Chapter 1 Introduction
Part I Wind Field Analysis
Chapter 2 A Single Time Series Model
Chapter 3 Spatiotemporal
Chapter 4 Regimeswitching
Part II Wind Turbine Performance Analysis
Chapter 5 Power Curve Modeling and Analysis
Chapter 6 Production Efficiency Analysis
Chapter 7 Quantification of Turbine Upgrade
Chapter 8 Wake Effect Analysis
Chapter 9 Overview of Turbine Maintenance Optimization
Chapter 10 Extreme Load Analysis
Chapter 11 Computer Simulator Based Load Analysis
Chapter 12 Anomaly Detection and Fault Diagnosis
Klappentext
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.
