Data Science for Wind Energy
Proposal review
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
Keywords
Bayesian Additive Regression Trees; SVM Model; Power Curve Model; Wind Speed; GEV Distribution; PACF Plot; Wind Turbine; Binning Method; Local Wind Field; ARMA Model; Wind Field Analysis; Ahead Forecast; Wind Speed Forecast; Power Curve; Wind Farm; Data Science Methods; Test Turbine; Importance Sampling Density; Be; GMRF Model; Computer Simulators; CMC; Power Coefficient; Importance Sampling Method; Posterior Predictive DistributionDOI
10.1201/9780429490972ISBN
9780429956515, 9781138590526, 9780429956492, 9780429956508, 9780367729097, 9780429490972, 9780429956515OCN
1103917723Publisher
Taylor & FrancisPublisher website
https://taylorandfrancis.com/Publication date and place
2019Imprint
Chapman and Hall/CRCClassification
Data mining
Alternative and renewable energy sources and technology
Artificial intelligence
Probability and statistics