Short-term wind speed forecasting model based on an attention-gated
  recurrent neural network and error correction strategy
Haojian Huang
The accurate wind speed series forecast is very pivotal to security of grid
dispatching and the application of wind power. Nevertheless, on account of
their nonlinear and non-stationary nature, their short-term forecast is
extremely challenging. Therefore, this dissertation raises one short-term wind
speed forecast pattern on the foundation of attention with an improved gated
recurrent neural network (AtGRU) and a tactic of error correction. That model
uses the AtGRU model as the preliminary predictor and the GRU model as the
error corrector. At the beginning, SSA (singular spectrum analysis) is employed
in previous wind speed series for lessening the noise. Subsequently, historical
wind speed series is going to be used for the predictor training. During this
process, the prediction can have certain errors. The sequence of these errors
processed by variational modal decomposition (VMD) is used to train the
corrector of error. The eventual forecast consequence is just the sum of
predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model
outperforms the compared models in three case studies on Woodburn, St. Thomas,
and Santa Cruz. It is indicated that the model evidently enhances the
correction of the wind speed forecast.