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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.
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
