English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting