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Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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

Fan, Fulin and Bell, Keith and Hill, David and Infield, David (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.

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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.