Data Science for Wind Energy: 1st Edition (Hardback) book cover

Data Science for Wind Energy

1st Edition

By Yu Ding

Chapman and Hall/CRC

388 pages | 103 B/W Illus.

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Hardback: 9781138590526
pub: 2019-06-06
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This book provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, from 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 will be covered, including time series models, spatio-temporal model and analysis, kernel regression, decision trees, k-NN, splines, Bayesian inference, and MCMC sampling. More importantly, the data science methods will be described in the context of wind energy applications, with specific wind energy examples and case studies.

Table of Contents

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

About the Author

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.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Database Management / Data Mining
TECHNOLOGY & ENGINEERING / Power Resources / Alternative & Renewable