Enhancing wind power forecast precision via multi-head attention transformer: an investigation on single-step and multi-step forecasting
Files
(Published version)
Date
2023
Authors
Sarkar, M.D.R.
Anavatti, S.
Dam, T.
Pratama, M.
Kindhi, B.A.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
International Joint Conference on Neural Networks, 2023, vol.2023-June, pp.1-8
Statement of Responsibility
Conference Name
2023 International Joint Conference on Neural Networks (IJCNN) (18 Jun 2023 - 23 Jun 2023 : Gold Coast Australia)
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2023 IEEE
Access Condition Notes: Accepted manuscript is available open access