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    Data Science for Wind Energy

    Proposal review

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    Author(s)
    Ding, Yu
    Language
    English
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    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
    URI
    https://library.oapen.org/handle/20.500.12657/101466
    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 Distribution
    DOI
    10.1201/9780429490972
    ISBN
    9780429956515, 9781138590526, 9780429956492, 9780429956508, 9780367729097, 9780429490972, 9780429956515
    OCN
    1103917723
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2019
    Grantor
    • Georgia Institute of Technology - [...]
    Imprint
    Chapman and Hall/CRC
    Classification
    Data mining
    Alternative and renewable energy sources and technology
    Artificial intelligence
    Probability and statistics
    Pages
    424
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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