ABSTRACT

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

chapter Chapter 1|14 pages

Introduction

part I|1 pages

Wind Field Analysis

chapter Chapter 2|40 pages

A Single Time Series Model

chapter Chapter 3|36 pages

Spatio-temporal Models

chapter Chapter 4|30 pages

Regime-switching Methods for Forecasting

part II|1 pages

Wind Turbine Performance Analysis

chapter Chapter 5|34 pages

Power Curve Modeling and Analysis

chapter Chapter 6|28 pages

Production Efficiency Analysis and Power Curve

chapter Chapter 7|32 pages

Quantification of Turbine Upgrade

chapter Chapter 8|26 pages

Wake Effect Analysis

part III|1 pages

Wind Turbine Reliability Management

chapter Chapter 9|20 pages

Overview of Wind Turbine Maintenance Optimization

chapter Chapter 10|34 pages

Extreme Load Analysis

chapter Chapter 11|30 pages

Computer Simulator-Based Load Analysis

chapter Chapter 12|36 pages

Anomaly Detection and Fault Diagnosis