A widely linear multichannel Wiener filter for wind prediction

Research output: Contribution to conferencePaper

2 Citations (Scopus)

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.

Conference

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

Fingerprint

Time measurement
Time series

Keywords

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

Cite this

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
Dowell, Jethro ; Weiss, Stephan ; Infield, David ; Chandna, Swati. / A widely linear multichannel Wiener filter for wind prediction. Paper presented at 2014 IEEE Workshop on Statistical Signal Processing (SSP) , Gold Coast, United Kingdom.4 p.
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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.",
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note = "{\circledC} 2014 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.; 2014 IEEE Workshop on Statistical Signal Processing (SSP) ; Conference date: 29-06-2014 Through 02-07-2014",
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Dowell, J, Weiss, S, Infield, D & Chandna, S 2014, 'A widely linear multichannel Wiener filter for wind prediction' Paper presented at 2014 IEEE Workshop on Statistical Signal Processing (SSP) , Gold Coast, United Kingdom, 29/06/14 - 2/07/14, pp. 29-32. https://doi.org/10.1109/SSP.2014.6884567

A widely linear multichannel Wiener filter for wind prediction. / Dowell, Jethro; Weiss, Stephan; Infield, David; Chandna, Swati.

2014. 29-32 Paper presented at 2014 IEEE Workshop on Statistical Signal Processing (SSP) , Gold Coast, United Kingdom.

Research output: Contribution to conferencePaper

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