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

Li, L.-L., Liu, Z.-F., Tseng, M.-L., Jantarakolica, K. and Lim, M. K. (2021) Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power. Expert Systems with Applications, 184, 115579. (doi: 10.1016/j.eswa.2021.115579)

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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.

Item Type:Articles
Additional Information:This study was supported by the key project of 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].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lim, Professor Ming
Creator Roles:
Lim, M.Writing – original draft, Writing – review and editing
Authors: Li, L.-L., Liu, Z.-F., Tseng, M.-L., Jantarakolica, K., and Lim, M. K.
College/School:College of Social Sciences > Adam Smith Business School > Management
Journal Name:Expert Systems with Applications
Publisher:Elsevier
ISSN:0957-4174
ISSN (Online):1873-6793
Published Online:11 July 2021
Copyright Holders:Copyright © 2021 Elsevier Ltd.
First Published:First published in Expert Systems with Applications 184: 115579
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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