Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies

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

5 Citations (Scopus)

Abstract

This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model.
LanguageEnglish
Title of host publication2015 IEEE Eindhoven PowerTech Proceedings
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 2 Jul 2015
EventIEEE PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

ConferenceIEEE PowerTech 2015
CountryNetherlands
CityEindhoven
Period29/06/152/07/15

Fingerprint

Overhead lines
Interpolation
Cooling

Keywords

  • dynamic line rating
  • kriging
  • vector auto-regressive models
  • de-trending

Cite this

Fan, Fulin ; Bell, Keith ; Hill, David ; Infield, David. / Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies. 2015 IEEE Eindhoven PowerTech Proceedings. Piscataway, NJ. : IEEE, 2015. pp. 1-6
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title = "Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies",
abstract = "This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model.",
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author = "Fulin Fan and Keith Bell and David Hill and David Infield",
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Fan, F, Bell, K, Hill, D & Infield, D 2015, Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies. in 2015 IEEE Eindhoven PowerTech Proceedings. IEEE, Piscataway, NJ., pp. 1-6, IEEE PowerTech 2015, Eindhoven, Netherlands, 29/06/15. https://doi.org/10.1109/PTC.2015.7232348

Wind forecasting using kriging and vector auto-regressive models for dynamic line rating studies. / Fan, Fulin; Bell, Keith; Hill, David; Infield, David.

2015 IEEE Eindhoven PowerTech Proceedings. Piscataway, NJ. : IEEE, 2015. p. 1-6.

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

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N1 - © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.

PY - 2015/7/2

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N2 - This paper aims to describe methods to forecast wind speeds experienced around overhead lines (OHLs) in order to predict the wind cooling effect and thus the dynamic line ratings (DLRs) of OHLs. The wind speed at a particular OHL span is forecast through a kriging interpolation between the wind speed predictions produced by a vector auto-regressive (VAR) model for a limited number of weather stations at which observations have been obtained. A temporal de-trending method is used to ensure the stationarity of de-trended data from which model parameters are determined. A spatial de-trending method is adopted in a kriging model. The results show that the kriging model performs better than the inverse distance weighting (IDW) method and that the spatial de-trending makes the main contribution to the accuracy of interpolation. Furthermore, the VAR forecasting model is shown to give greater improvement over persistence than a simple auto-regressive (AR) model.

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