A widely linear multichannel Wiener filter for wind prediction

Dowell, Jethro and Weiss, Stephan and Infield, David and Chandna, Swati (2014) A widely linear multichannel Wiener filter for wind prediction. In: 2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014-06-29 - 2014-07-02, Australia. (https://doi.org/10.1109/SSP.2014.6884567)

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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.

ORCID iDs

Dowell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X, Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206, Infield, David and Chandna, Swati;
  • Item type:Conference or Workshop Item(Paper)
    ID code:49434
    Dates:
    Date
    Event
    July 2014
    Published
    Notes:© 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.
    Keywords:Wiener filters, widely linear processing, weather forecasting, atmospheric techniques, wind, Electrical Engineering. Electronics Nuclear Engineering, Electrical and Electronic Engineering, Energy Engineering and Power Technology
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Technology and Innovation Centre > Sensors and Asset Management
    Depositing user: Pure Administrator
    Date deposited:01 Oct 2014 08:25
    Last modified:19 Mar 2023 04:20
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/49434
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