 Wind power forecasting using attention-based gated recurrent unit networkZhewen Niu, Zeyuan Yu, Wenhu Tang, Qinghua Wu and Marek Reformat Energy, 2020, vol. 196, issue C Abstract: Wind power forecasting (WPF) plays an increasingly essential role in power system operations. So far, most forecasting models have focused on a single-step-ahead WPF, and the obtained results are insufficient for planning and operations of the power system due to the intermittent and fluctuated nature of wind. At the same time, most of the current multi-step-ahead WPF models neglect the correlation between different forecasting tasks. In this paper, we propose a novel sequence-to-sequence model using the Attention-based Gated Recurrent Unit (AGRU) that improves accuracy of forecasting processes. It embeds the task of correlating different forecasting steps by hidden activations of GRU blocks. In addition, an attention mechanism is designed as a feature selection method to identify the most important input variables. To validate the effectiveness of the proposed AGRU model, three different case studies focused on forecasting accuracy, computational efficiency, and feature selection abilities are carried out. Their performances are compared with various wind power forecasting benchmarks. Keywords: Wind power forecasting; Recurrent neural network; Feature selection; Attention mechanism; Deep learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301882 DOI: 10.1016/j.energy.2020.117081 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.