Data Science for Wind Energy

Yu Ding (Texas A&M University, USA)

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English

Chapman & Hall/CRC
18 December 2020

Probability & statistics; Alternative & renewable energy sources & technology; Environmental science, engineering & technology; Databases; Artificial intelligence

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.

By: Yu Ding (Texas A&M University USA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9780367729097
ISBN 10: 0367729091
Pages: 424
Publication Date: 18 December 2020
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Paperback
Publisher's Status: Active

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.

Reviews for Data Science for Wind Energy

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