Kernel methods for short-term spatio-temporal wind prediction

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 wi...

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Bibliographic Details
Published in:2015 IEEE Power & Energy Society General Meeting pp. 1 - 5
Main Authors: Dowell, Jethro, Weiss, Stephan, Infield, David
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2015
Subjects:
Benchmark testing
Dictionaries
Kernel
Least squares approximations
Prediction algorithms
Wind forecasting
Wind speed
ISSN:1932-5517
Online Access:Get full text
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Summary: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.
ISSN:1932-5517
DOI:10.1109/PESGM.2015.7285965