doi:10.16186/j.cnki.1673-9787.2021110135

Received:2023/11/30

Revised:2023/05/26

Published:2024/03/25

Short-term wind power prediction based on VMD-LILGWO-LSSVM

WANG Rui1，LI Hongrui1，LU Jing1，BU Xuhui2

1.School of Computer Science and Technology，Henan Polytechnic University，Jiaozuo 454000，Henan，China；2.School of Electrical Engineering and Automation，Henan Polytechnic University，Jiaozuo 454000，Henan，China

Abstract: Objectives In order to reduce frequency fluctuations caused by wind power integration into the national grid and improve the accuracy of wind power prediction， Methods a short-term wind power prediction method combining variational modal decomposition（VMD），lens imaging learning grey wolf optimizer（LILGWO）and least squares support vector machine（LSSVM）was proposed.Firstly，the wind power sequence was decomposed into three modal components with different complexity by VMD method to reduce the fluctuation of wind power.Then LSSVM was used to mine the feature information of each component，which was predicted separately.For the problem that the selection of important parameters in LSSVM model had a large impact on the prediction accuracy，LILGWO was introduced to optimize the parameters.Finally，the prediction results of each component were superimposed and reconstructed to obtain the final predicted wind power. Results Taking the actual wind power station data in Ningxia Hui Autonomous Region as an example， average the predictions for the next three days. The prediction mean absolute error（MAE） of the proposed method in this article was 2.706 8 kW，the root mean square error （RMSE） was 2.0211，and the coefficient of fit determination（R-Square，R2） was 0.976 9.Compared with the comparison methods 3~6 mentioned in the article， the RMSE of the method decreased by 40.93%，25.21%，14.7%，and 6.24%，respectively；MAE decreased by 42.34%，28.04%，16.97%，and 7.77%，respectively；R2 increased by 4.21%，1.78%，0.82%，and 0.28%，respectively.In terms of prediction time，the average training time for BP and LSSVM was 10 seconds and 138 seconds，respectively.Although LSSVM had a longer prediction time，it performed the best.PSO，GWO and LILGWO were used to optimize LSSVM，and the training time was shortened by an average of 39 seconds，44 seconds，and 58 seconds，respectively. Conclusions The effectiveness of the decomposition algorithm proposed in this paper had been verified through comparative experiments.The lens imaging learning grey wolf optimizer algorithm proposed in this paper had excellent optimization ability for the key parameters of least squares support vector machines.The effectiveness of the proposed method in short-term wind power prediction was verified by simulation.

Key words: wind power；short-term prediction；variational mode decomposition；approximate entropy；lens imaging learning grey wolf optimizer algorithm；least squares support vector machine