Probabilistic weather forecasting for dynamic line rating studies

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

7 Citations (Scopus)
94 Downloads (Pure)

Abstract

This paper aims to describe methods to determine short term probabilistic forecasts of weather conditions experienced at overhead lines (OHLs) in order to predict percentiles of dynamic line ratings of OHLs which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive probability distributions of air temperature, wind speed and direction are assumed to be normal, truncated normal and von Mises respectively. Predictive centres are estimated as a sum of residuals predicted by a univariate auto-regressive model or a vector auto-regressive model and temporal trends fitted by a Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. Parameters of the probabilistic models are determined to minimize the average value of continuous ranked probability score which is a summary indicator to assess performance of probabilistic models. The conditionally heteroscedastic models are shown to have appropriate sharpness and better calibration than the respective homoscedastic models.
Original languageEnglish
Title of host publication2016 Power Systems Computation Conference (PSCC)
Place of PublicationPiscataway. NJ.
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Electronic)9788894105124
DOIs
Publication statusPublished - 20 Jun 2016
Event19th Power Systems Computation Conference - Porto Antico Conference Centre, Genoa, Italy
Duration: 20 Jun 201624 Jun 2016
http://www.pscc2016.net/

Conference

Conference19th Power Systems Computation Conference
Abbreviated titlePSCC 2016
CountryItaly
CityGenoa
Period20/06/1624/06/16
Internet address

Keywords

  • dynamic line rating
  • probabilistic forecasting
  • auto-regressive models
  • conditional heteroscedasticity
  • continuous ranked probability score

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  • Research Output

    • 7 Citations
    • 1 Doctoral Thesis

    Enhanced Weather Modelling for Dynamic Line Rating

    Fan, F., 6 Nov 2018, Glasgow: University of Strathclyde. 278 p.

    Research output: ThesisDoctoral Thesis

    Open Access
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