Abstract
A linear, time-varying autoregressive (AR) process is used to model
and forecast wind speed. This modelling approach takes into account
the non-stationary nature of wind speed, The time-varying parameters
of the AR model are modelled by smoothed, integrated random walk
processes. A Kalman filter is used to estimate the time-varying parameters
of the AR model. The algorithm is used to forecast wind speed from
1 h to a few hours ahead.
Users
Please
log in to take part in the discussion (add own reviews or comments).