Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC

Tao Liang, Chunjie Chai, Hexu Sun and Jianxin Tan

Energy, 2022, vol. 250, issue C

Abstract: To additional understand the wind speed prediction of every wind energy facility in several geographical locations and environments within the Wind Power Centralized Control Center (WPCCC), this paper proposes a variable short wind speed prediction model of Capsule Neural Network (Capsnet) and bidirectional Long and Short Term Memory Network (BILSTM) combined with Multi-Object Harris Hawk optimization (MOHHO). The method selects four typical location wind farms within the wind farm hub and uses their historical wind speed information and multidimensional meteorologic variables as inputs to the model, then extract the spatial features of the multifaceted temporal variables by Capsnet and apply BILSTM to simulate the time dependence of the capsule layer outputs, and at last transfer the four pre-trained models to the wind farm hub victimization the transfer learning and MOHHO optimization algorithm to weight the four sets of prediction results, to get the wind speed prediction results of any wind farm underneath the jurisdiction of the centralized center. Once examination of the experimental results, it's shown that the model has high prediction accuracy.

Keywords: Wind speed prediction; Wind power centralized control center; Capsnet; BILSTM; Transfer learning; MOHHO (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222006648
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:250:y:2022:i:c:s0360544222006648

DOI: 10.1016/j.energy.2022.123761

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 ().