A widely linear multichannel Wiener filter for wind prediction

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Abstract

The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modelled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel Wiener prediction filter. Building on a C-linear cyclo-stationary predictor, a new widely linear filter is developed and tested on hourly mean wind speed and direction measurements made at 13 locations in the UK over 6 years. The new predictor shows a reduction in mean squared error at all locations. Furthermore it is found that the scale of that reduction strongly depends on conditions local to the measurement site.
Original languageEnglish
Pages29-32
Number of pages4
DOIs
Publication statusPublished - Jul 2014
Event2014 IEEE Workshop on Statistical Signal Processing (SSP) - Australia, Gold Coast, United Kingdom
Duration: 29 Jun 20142 Jul 2014

Conference

Conference2014 IEEE Workshop on Statistical Signal Processing (SSP)
CountryUnited Kingdom
CityGold Coast
Period29/06/142/07/14

Keywords

  • Wiener filters
  • widely linear processing
  • weather forecasting
  • atmospheric techniques
  • wind

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    Dowell, J., Weiss, S., Infield, D., & Chandna, S. (2014). A widely linear multichannel Wiener filter for wind prediction. 29-32. Paper presented at 2014 IEEE Workshop on Statistical Signal Processing (SSP) , Gold Coast, United Kingdom. https://doi.org/10.1109/SSP.2014.6884567