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.
Bibliographical noteFunding Information:
Manuscript received May 13, 2020; revised September 3, 2020 and November 4, 2020; accepted December 2, 2020. Date of publication December 10, 2020; date of current version March 22, 2021. This work was supported in part by the Framework of International Cooperation Program Managed by National Research Foundation of Korea under Grant 2017K1A4A3013579 and in part by the “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) Granted Financial Resource from the Ministry of Trade, Industry & Energy, Republic of Korea under Grant 20194030202420. Paper no. TSTE-00500-2020. (Corresponding author: Kyeon Hur.) Min-Seung Ko, Chang Woo Hong, and Kyeon Hur are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
© 2010-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment