Explainable temporal dependence in multi-step wind power forecast via decomposition based chain echo state networks

Zhou Wu, Shaoxiong Zeng, Ruiqi Jiang, Haoran Zhang and Zhile Yang

Energy, 2023, vol. 270, issue C

Abstract: Wind power is one of the most promising renewable energy for its abundant resources, economically competitive, and environmentally friendly. Nevertheless, the wind power is challenging used in the power generation system due to its intermittency. Therefore, to improve the utilization ratio of wind power, the common method is adopting a prediction model for scheduling the generation industries. However, the information offered by single-step models hardly assists managers control their generators, and existing multi-step prediction models ignore the temporal dependence among predicted steps. Thus, a hybrid method based on a deep-chain echo state network (DCESN) and variational mode decomposition (VMD) is proposed to enhance the mapping capability for wind power multi-step prediction. The multiple reservoirs of deep-chain echo state network are concatenated as a chain structure, which could congregate the temporal relations among future steps shown in visualized graphs. Three comparative experiments demonstrate that the proposed hybrid method has promising performance on wind power multi-step prediction.

Keywords: Multi-step prediction; Echo state network; Hybrid model; Wind power (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223003006
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003006

DOI: 10.1016/j.energy.2023.126906

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().