1st Edition

Data Science for Wind Energy

By Yu Ding Copyright 2019
    424 Pages
    by Chapman & Hall

    424 Pages 103 B/W Illustrations
    by Chapman & Hall

    424 Pages 103 B/W Illustrations
    by Chapman & Hall

    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.


    • 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


    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


    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.

    "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