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Présentation Data Science For Wind Energy de Yu Ding Format Relié
- Livre Technologie
Résumé : 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
Biographie: 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, and a Fellow of the Institute of Industrial & Systems Engineers and the American Society of Mechanical Engineers
Sommaire: 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
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