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• W1660049061 abstract "Two nonlinear methods for producing short-term spatio-temporal wind speed forecast are presented. From the relatively new class of kernel methods, a kernel least mean squares algorithm and kernel recursive least squares algorithm are introduced and used to produce 1 to 6 hour-ahead predictions of wind speed at six locations in the Netherlands. The performance of the proposed methods are compared to their linear equivalents, as well as the autoregressive, vector autoregressive and persistence time series models. The kernel recursive least squares algorithm is shown to offer significant improvement over all benchmarks, particularly for longer forecast horizons. Both proposed algorithms exhibit desirable numerical properties and are ripe for further development." @default.
• W1660049061 created "2016-06-24" @default.
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• W1660049061 date "2015-07-01" @default.
• W1660049061 modified "2023-09-24" @default.
• W1660049061 title "Kernel methods for short-term spatio-temporal wind prediction" @default.
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• W1660049061 doi "https://doi.org/10.1109/pesgm.2015.7285965" @default.
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