Bayesian inferencing for wind resource characterisation

Miranda S. Marcos , Gavin Shaddick, Rod W. Dunn, F. Li, K.R.W. Bell

Research output: Contribution to conferencePaper

14 Citations (Scopus)
66 Downloads (Pure)


The growing role of wind power in power systems has motivated R&D on methodologies to characterise the wind resource at sites for which no wind speed data is available. Applications such as feasibility assessment of prospective installations and system integration analysis of future scenarios, amongst others, can greatly benefit from such methodologies. This paper focuses on the inference of wind speeds for such potential sites using a Bayesian approach to characterise the spatial distribution of the resource. To test the approach, one year of wind speed data from four weather stations was modelled and used to derive inferences for a fifth site. The methodology used is described together with the model employed and simulation results are presented and compared to the data available for the fifth site. The results obtained indicate that Bayesian inference can be a useful tool in spatial characterisation of wind.
Original languageEnglish
Pages1 - 6
Number of pages6
Publication statusPublished - 21 May 2007
EventPMAPS 2006 - 9th International Conference on Probabilistic Methods Applied to Power Systems - Stockholm , Sweden
Duration: 11 Jun 200615 Jun 2006


ConferencePMAPS 2006 - 9th International Conference on Probabilistic Methods Applied to Power Systems


  • bayesian inference
  • Markov chain Monte Carlo simulation
  • spatio-temporal modelling
  • statistical methods
  • wind power generation
  • wind speed


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