Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy

Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional-order Beetle swarm optimization algorithm is proposed to optimiz...

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Main Authors: Shuren Pu, Yuanchen Gao, Yifeng Sun, Chang Liu, Siyu He, Fengjiao Wu, Delan Zhu, Bin Wang
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2024/2822223
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author Shuren Pu
Yuanchen Gao
Yifeng Sun
Chang Liu
Siyu He
Fengjiao Wu
Delan Zhu
Bin Wang
author_facet Shuren Pu
Yuanchen Gao
Yifeng Sun
Chang Liu
Siyu He
Fengjiao Wu
Delan Zhu
Bin Wang
author_sort Shuren Pu
collection DOAJ
description Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional-order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. To weaken the negative impact of wind speed volatility on the accuracy of the prediction model, a two-stage modal decomposition strategy based on extreme-point symmetric mode decomposition, FO-BSO algorithm, and VMD is proposed. ESMD is used to decompose the original wind speed time series. The diversity entropy is introduced as the criterion, and the FO-BSO-VMD secondary decomposition is applied for the high-entropy modal components to further weaken the volatility of wind speed. Simulation results show that compared with the original LSSVM model, the ESMD-FO-BSO-VMD-LSSVM model presented in this study exhibits an average 44.10% increase in the fitting degree of wind speed prediction over three steps, which verifies the superior performance of the model in short-term multistep wind speed prediction.
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id doaj-art-4f1af5ef87784b45a5a10af30b06e13e
institution Kabale University
issn 2090-0155
language English
publishDate 2024-01-01
publisher Wiley
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series Journal of Electrical and Computer Engineering
spelling doaj-art-4f1af5ef87784b45a5a10af30b06e13e2025-02-03T05:15:52ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/2822223Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition StrategyShuren Pu0Yuanchen Gao1Yifeng Sun2Chang Liu3Siyu He4Fengjiao Wu5Delan Zhu6Bin Wang7College of Water Resources and Architectural EngineeringHydroelectric and Pumped Storage Engineering InstituteCollege of Water Resources and Architectural EngineeringCollege of Water Resources and Architectural EngineeringCollege of Water Resources and Architectural EngineeringCollege of Water Resources and Architectural EngineeringCollege of Water Resources and Architectural EngineeringCollege of Water Resources and Architectural EngineeringLeast squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional-order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. To weaken the negative impact of wind speed volatility on the accuracy of the prediction model, a two-stage modal decomposition strategy based on extreme-point symmetric mode decomposition, FO-BSO algorithm, and VMD is proposed. ESMD is used to decompose the original wind speed time series. The diversity entropy is introduced as the criterion, and the FO-BSO-VMD secondary decomposition is applied for the high-entropy modal components to further weaken the volatility of wind speed. Simulation results show that compared with the original LSSVM model, the ESMD-FO-BSO-VMD-LSSVM model presented in this study exhibits an average 44.10% increase in the fitting degree of wind speed prediction over three steps, which verifies the superior performance of the model in short-term multistep wind speed prediction.http://dx.doi.org/10.1155/2024/2822223
spellingShingle Shuren Pu
Yuanchen Gao
Yifeng Sun
Chang Liu
Siyu He
Fengjiao Wu
Delan Zhu
Bin Wang
Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
Journal of Electrical and Computer Engineering
title Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
title_full Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
title_fullStr Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
title_full_unstemmed Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
title_short Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy
title_sort multistep wind speed prediction based on the fractional order longicorn swarm algorithm and the two stage modal decomposition strategy
url http://dx.doi.org/10.1155/2024/2822223
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