Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

Ling-Ling Li; Zhi-Feng Liu; Ming-Lang Tseng; Korbkul Jantarakolica; Ming Lim

doi:10.1016/j.eswa.2021.115579

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

Ling-Ling Li, Zhi-Feng Liu, Ming-Lang Tseng , Korbkul Jantarakolica, Ming Lim

Research Centre for Business in Society

Research output: Contribution to journal › Article › peer-review

68 Citations (Scopus)

223 Downloads (Pure)

Abstract

The strong volatility and randomness of wind power impact the grid and reduce the voltage quality of the grid when wind power is connected to the grid in large scale. The power sector takes the wind abandonment measures to ensure the grid voltage stability. The enhanced crow search algorithm optimization-extreme learning machine (ENCSA-ELM) model is proposed to accurately forecast short-term wind power to improve the utilization efficiency of clean energy. (1) The enhanced crow search algorithm (ENCSA) is proposed and applied to short-term wind power forecast. The convergence performance test revealed that the local development and global exploration capabilities of the ENCSA were enhanced, and the test result of the proposed ENCSA algorithm outperformed other well-known nature inspired algorithms and state-of-the-art CSA variants; (2) The output and input of the forecasting models were determined by analysis of the wind power impact factors and the wind power samples in autumn, winter and spring were forecasted by the ENCSA-ELM model; and (3) The forecast results were analyzed by multiple evaluation indexes. The simulation experiments revealed that the error interval and evaluation indexes of the ENCSA-ELM model outperformed the state-of-the-art wind power forecast methods, traditional machine learning models and ELM optimized by other algorithms. The RMSE value and MAPE value of the proposed model were controlled below 20% and 4%. Accurate wind power prediction maintains the voltage stability of power grid and increases the utilization efficiency of clean energy.

Original language	English
Article number	115579
Number of pages	16
Journal	Expert Systems with Applications
Volume	184
Early online date	11 Jul 2021
DOIs	https://doi.org/10.1016/j.eswa.2021.115579
Publication status	Published - Dec 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Funder

Tianjin Natural Science Foundation [Project No. 19JCZDJC32100 ], the Hebei Provincial Innovation Foundation for Postgraduate [Project No. CXZZBS2020028 ] and the Natural Science Foundation of Hebei Province of China [Project No. E2018202282 ]

Funding

Funders	Funder number
Natural Science Foundation of Hebei Province	E2018202282
Natural Science Foundation of Tianjin City	19JCZDJC32100
Hunan Provincial Innovation Foundation for Postgraduate	CXZZBS2020028

Keywords

Clean energy
Enhanced crow search algorithm optimization
Extreme learning machine
Forecasting
Short-term wind power

ASJC Scopus subject areas

General Engineering
Computer Science Applications
Artificial Intelligence

Access to Document

10.1016/j.eswa.2021.115579

Post-PrintAccepted author manuscript, 4.41 MBLicence: CC BY-NC-ND

Cite this

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abstract = "The strong volatility and randomness of wind power impact the grid and reduce the voltage quality of the grid when wind power is connected to the grid in large scale. The power sector takes the wind abandonment measures to ensure the grid voltage stability. The enhanced crow search algorithm optimization-extreme learning machine (ENCSA-ELM) model is proposed to accurately forecast short-term wind power to improve the utilization efficiency of clean energy. (1) The enhanced crow search algorithm (ENCSA) is proposed and applied to short-term wind power forecast. The convergence performance test revealed that the local development and global exploration capabilities of the ENCSA were enhanced, and the test result of the proposed ENCSA algorithm outperformed other well-known nature inspired algorithms and state-of-the-art CSA variants; (2) The output and input of the forecasting models were determined by analysis of the wind power impact factors and the wind power samples in autumn, winter and spring were forecasted by the ENCSA-ELM model; and (3) The forecast results were analyzed by multiple evaluation indexes. The simulation experiments revealed that the error interval and evaluation indexes of the ENCSA-ELM model outperformed the state-of-the-art wind power forecast methods, traditional machine learning models and ELM optimized by other algorithms. The RMSE value and MAPE value of the proposed model were controlled below 20% and 4%. Accurate wind power prediction maintains the voltage stability of power grid and increases the utilization efficiency of clean energy.",

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AU - Lim, Ming

N1 - © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

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Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

Abstract

Bibliographical note

Funder

Funding

Keywords

ASJC Scopus subject areas

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