  The main objective of this study is to propose an enhanced wind power
forecasting (EWPF) transformer model for handling power grid operations and
boosting power market competition. It helps reliable large-scale integration of
wind power relies in large part on accurate wind power forecasting (WPF). The
proposed model is evaluated for single-step and multi-step WPF, and compared
with gated recurrent unit (GRU) and long short-term memory (LSTM) models on a
wind power dataset. The results of the study indicate that the proposed EWPF
transformer model outperforms conventional recurrent neural network (RNN)
models in terms of time-series forecasting accuracy. In particular, the results
reveal a minimum performance improvement of 5% and a maximum of 20% compared to
LSTM and GRU. These results indicate that the EWPF transformer model provides a
promising alternative for wind power forecasting and has the potential to
significantly improve the precision of WPF. The findings of this study have
implications for energy producers and researchers in the field of WPF.
