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oapen-20.500.12657-874202024-03-28T14:03:12Z Data Science for Wind Energy Ding, Yu Bayesian Additive Regression Trees;SVM Model;data mining;Power Curve Model;data analytics;Wind Speed;renewable energy;GEV Distribution;wind turbines;PACF Plot;machine learning;Wind Turbine;bayesian methods;Binning Method;data science methods;Local Wind Field;wind energy applications;ARMA Model;turbine reliability assessment;Wind Field Analysis;near-ground wind field analysis;Ahead Forecast;Wind Speed Forecast;Power Curve;Wind Farm;Test Turbine;Importance Sampling Density;Be;GMRF Model thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics 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 2024-02-01T12:34:17Z 2024-02-01T12:34:17Z 2020 book 9781138590526 9780429956492 9780429956508 9780367729097 9780429490972 https://library.oapen.org/handle/20.500.12657/87420 eng application/pdf Attribution-NonCommercial-NoDerivatives 4.0 International 9780429956515.pdf Taylor & Francis CRC Press 10.1201/9780429490972 10.1201/9780429490972 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb e9f4faa3-9aac-40dd-b63b-aec2d8ab48ad 9781138590526 9780429956492 9780429956508 9780367729097 9780429490972 CRC Press 425 Georgia Institute of Technology Georgia Tech open access
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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
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