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    Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks

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    Type
    Preprint
    Authors
    Huang, Huang cc
    Castruccio, Stefano
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR2018-CRG7-3742
    Date
    2021-02-01
    Permanent link to this record
    http://hdl.handle.net/10754/667220
    
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    Abstract
    Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modeling wind at a high resolution brings forth considerable challenges given its turbulent and highly nonlinear dynamics. In developing countries where wind farms over a large domain are currently under construction or consideration, this is even more challenging given the necessity of modeling wind over space as well. In this work, we propose a machine learning approach to model the nonlinear hourly wind dynamics in Saudi Arabia with a domain-specific choice of knots to reduce the spatial dimensionality. Our results show that for locations highlighted as wind abundant by a previous work, our approach results in a 11% improvement in the two-hours-ahead forecasted power against operational standards in the wind energy sector, yielding a saving of nearly one million US dollars over a year under current market prices in Saudi Arabia.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR2018-CRG7-3742.
    Publisher
    arXiv
    arXiv
    2102.01141
    Additional Links
    https://arxiv.org/pdf/2102.01141
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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