Kernel methods for short-term spatio-temporal wind prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

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.

LanguageEnglish
Title of host publication2015 IEEE Power and Energy Society General Meeting
PublisherIEEE
Number of pages5
ISBN (Print)9781467380409
DOIs
Publication statusPublished - 30 Sep 2015
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States
Duration: 26 Jul 201530 Jul 2015

Conference

ConferenceIEEE Power and Energy Society General Meeting, PESGM 2015
CountryUnited States
CityDenver
Period26/07/1530/07/15

Fingerprint

Time series

Keywords

  • least squares approximation
  • wind power
  • kernel method
  • nonlinear methods

Cite this

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title = "Kernel methods for short-term spatio-temporal wind prediction",
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.",
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author = "Jethro Dowell and Stephan Weiss and David Infield",
note = "(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.",
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Dowell, J, Weiss, S & Infield, D 2015, Kernel methods for short-term spatio-temporal wind prediction. in 2015 IEEE Power and Energy Society General Meeting. IEEE, IEEE Power and Energy Society General Meeting, PESGM 2015, Denver, United States, 26/07/15. https://doi.org/10.1109/PESGM.2015.7285965

Kernel methods for short-term spatio-temporal wind prediction. / Dowell, Jethro; Weiss, Stephan; Infield, David.

2015 IEEE Power and Energy Society General Meeting. IEEE, 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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AB - 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.

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