1 research outputs found
A widely linear multichannel Wiener Filter for wind prediction
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 modeled 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