@inproceedings{ebc4b4acbbbe4dbb927be6f6492ede26,
title = "Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting",
abstract = "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.",
keywords = "GRU, LSTM, Transformer, Wind Power Forecasting",
author = "Sarkar, {Md Rasel} and Anavatti, {Sreenatha G.} and Tanmoy Dam and Mahardhika Pratama and Kindhi, {Berlian Al}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Joint Conference on Neural Networks, IJCNN 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/IJCNN54540.2023.10191444",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings",
address = "United States",
}