Fully connected multi-reservoir echo state networks for wind power prediction

Xianshuang Yao, Kangshuai Guo, Jianqi Lei and Xuanyu Li

Energy, 2024, vol. 312, issue C

Abstract: In this paper, considering the complex relationship between wind speed variation characteristics and data features, a fully connected multi-reservoir echo state network (FCMR-ESN) is proposed for wind power generation prediction, which can handle some issues such as insufficient extraction of data features and a gradual decline in memory capacity. Firstly, the Spearman correlation coefficient is used to calculate the correlation between the characteristic data. Secondly, the fully connected neurons are applied to the connections between the reservoirs. The fully connected layer can capture the complex nonlinear relationships in the wind power data and effectively map them to the prediction results. Thirdly, the reservoir parameters and the connection coefficients between the reservoirs are optimized using the improved weighted mean of vectors (INFO). Finally, The effectiveness of FCMR-ESN prediction is demonstrated through prediction experiments conducted on two sets of datasets with different lengths from different regions. In the one-day time prediction for the first set of data, the MAPE decreases to 2.90%. The MAPE for the three-day prediction in the second data set is reduced to 3.53%.

Keywords: Fully connected multi-reservoir; Echo state networks; INFO optimization algorithm; Wind power prediction (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224033577
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:312:y:2024:i:c:s0360544224033577

DOI: 10.1016/j.energy.2024.133579

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 ().