基于混合智能算法优化LSSVM的短期风压预测

doi:10.12066/j.issn.1007-2861.1919

Abstract

Abstract:

Least squares support vector machine (LSSVM)is used to predict wind pressure on building surfaces. To enhance the generalization performance and prediction accuracy of LSSVM for wind pressure, LSSVM based on combination of ant colony optimization (ACO) and particle swarm optimization (PSO) is proposed to find optimal parameters. The combination avoids shortcomings in ACO and PSO, and achieves complementary focus of both. Using LSSVM based on ACO+PSO,wind pressure is forecast. It is compared with ACO-based LSSVM and PSO-based LSSVM, respectively. The numerical analysis shows that the proposed method can improve prediction accuracy and robustness of LSSVM, and has good prospects in engineering applications.

Key words: wind pressure forecast, least square support vector machine (LSSVM), intelligent optimization, ant colony optimization (ACO), particle swarm optimization (PSO)

CLC Number:

TU311

TU Weiping, LI Chunxiang. Short-term wind pressure forecast using LSSVM based on hybrid intelligent algorithm optimization[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(2): 347-356.

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Short-term wind pressure forecast using LSSVM based on hybrid intelligent algorithm optimization

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