Deep Concatenated Residual Network with Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting

Min Seung Ko, Kwangsuk Lee, Jae Kyeong Kim, Chang Woo Hong, Zhao Yang Dong, Kyeon Hur

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)

Abstract

This paper presents a deep residual network for improving time-series forecasting models, indispensable to reliable and economical power grid operations, especially with high shares of renewable energy sources. Motivated by the potential performance degradation due to the overfitting of the prevailing stacked bidirectional long short-term memory (Bi-LSTM) layers associated with its linear stacking, we propose a concatenated residual learning by connecting the multi-level residual network (MRN) and DenseNet. This method further integrates long and short Bi-LSTM networks, ReLU, and SeLU for its activating function. Rigorous studies present superior prediction accuracy and parameter efficiency for the widely used temperature dataset as well as the actual wind power dataset. The peak value forecasting and generalization capability, along with the credible confidence range, demonstrate that the proposed model offers essential features of a time-series forecasting, enabling a general forecasting framework in grid operations. The source code of this paper can be found in https://github.com/MinseungKo/DRNet.git.

Original languageEnglish
Article number9290065
Pages (from-to)1321-1335
Number of pages15
JournalIEEE Transactions on Sustainable Energy
Volume12
Issue number2
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

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