Kernel Methods for Short-term Spatio-temporal Wind Prediction

Dowell, J. , Weiss, S. and Infield, D. (2015) Kernel Methods for Short-term Spatio-temporal Wind Prediction. In: 2015 IEEE Power and Energy Society General Meeting, Denver, CO, USA, 26-30 Jul 2015, ISBN 9781467380409 (doi: 10.1109/PESGM.2015.7285965)

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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.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Dowell, J., Weiss, S., and Infield, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Published Online:05 October 2015

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