Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM

doi:10.16182/j.issn1004731x.joss.22-FZ0924

Abstract

Abstract:

To effectively predict the short-term wind power and its fluctuation range, a prediction method based on hybrid algorithm-optimized support vector machine is proposed. Exploratory data analysis is used to preprocess the original wind speed data to improve the data quality. Chaotic map, Levy flight strategy and particle swarm optimization are used to improve the ant lion algorithm. The support vector machine model optimized by hybrid algorithm is used to predict the wind power. The experimental results show that, compared with the new wind power prediction model, the prediction error of the output results of the method is lower, and the wind power. prediction ability is better.

Key words: wind power prediction, combination prediction, ant lion algorithm, levy flight, chaotic map

CLC Number:

TP391.9

Yefeng Jiao, Yan Wang, Zhicheng Ji. Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM[J]. Journal of System Simulation, 2022, 34(12): 2535-2545.

Figures/Tables 11

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模型	收敛次数	适应度值
ALO-SVM	51	0.683 9
LP- ALO-SVM	50	0.453 7
BP-ALO-SVM	49	0.594 8
BL-ALO-SVM	51	0.483 2
BLP-ALO-SVM	47	0.285 4

模型	收敛次数	适应度值
WHO-SVM	50	0.893 8
ALO-SVM	51	0.683 9
BLP-ALO-SVM	48	0.337 1

模型	MAPE/%	MAE	RSME
WHO-SVM	1.488 3	0.523 1	0.615 0
ALO-SVM	1.328 9	0.483 3	0.564 0
BLP-ALO-SVM	0.871 4	0.315 9	0.394 1

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