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    A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting

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    2019_CastroCamilo-etal_arXiv.pdf
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    Type
    Article
    Authors
    Castro, Daniela
    Huser, Raphaël cc
    Rue, Haavard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics
    Statistics Program
    KAUST Grant Number
    OSR-CRG2017-3434
    Date
    2019-07-23
    Embargo End Date
    2020-07-23
    Permanent link to this record
    http://hdl.handle.net/10754/656160
    
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    Abstract
    Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuel-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature bring a great threat to the wind power production stability, and to the wind turbines themselves. Lately, much work has been done on developing models to forecast average wind speed values, yet surprisingly little has focused on proposing models to accurately forecast extreme wind speeds, which can damage the turbines. In this work, we develop a flexible spliced Gamma-Generalized Pareto model to forecast extreme and non-extreme wind speeds simultaneously. Our model belongs to the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. Our models are fast to fit and can describe both the bulk and the tail of the wind speed distribution while producing short-term extreme and non-extreme wind speed probabilistic forecasts. Supplementary materials accompanying this paper appear online.
    Citation
    Castro-Camilo, D., Huser, R., & Rue, H. (2019). A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting. Journal of Agricultural, Biological and Environmental Statistics, 24(3), 517–534. doi:10.1007/s13253-019-00369-z
    Sponsors
    We thank Amanda Hering for helpful suggestions, and for providing the wind speed data. We also extend our thanks to Thomas Opitz for helpful discussion. Support from the KAUST Supercomputing Laboratory and access to Shaheen is also gratefully acknowledged. This publication is based upon work supported by KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.
    Publisher
    Springer Nature
    Journal
    Journal of Agricultural, Biological and Environmental Statistics
    DOI
    10.1007/s13253-019-00369-z
    Additional Links
    http://link.springer.com/10.1007/s13253-019-00369-z
    ae974a485f413a2113503eed53cd6c53
    10.1007/s13253-019-00369-z
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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