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基于改进TLBO优化LSSVM的风电功率短期预测 被引量:2

Short-term prediction of wind power based on improved TLBO optimization LSSVM
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摘要 为提高风电功率短期预测的精度,提出一种基于改进TLBO优化LSSVM的风电功率短期预测方法。首先对基本TLBO算法中的‘教’阶段进行改进,在采用自适应教学因子的同时改变所有搜索个体的平均值,从而能够自适应的提高TLBO在整个搜索空间的性能;然后改进TLBO算法的‘学’阶段,为维持种群的多样性,避免TLBO算法过早收敛和陷入局部最优,在学习阶段引入高斯变异算子;最后用改进的TLBO优化构建的LSSVM预测模型。以上海北沿风电场和莱州风电场实测数据为例,仿真结果表明,与PSO和TLBO优化LSSVM相比,改进的TLBO优化LSSVM方法对短期风电功率预测具有更好的稳定性和更高的准确性。 In order to improve the accuracy of short-term prediction of wind power,a short-term prediction method based on improved TLBO optimization LSSVM is proposed in this paper.Firstly,to improve TLBO algorithm of teaching stage,meanwhile,the adaptive teaching factor is used to change average of all search individuals,which can enhance the performance of TLBO in the whole search space.Then,the learning stage of TLBO algorithm is improved to maintain the diversity of the population and avoid the premature convergence and local optimization of TLBO algorithm,and the Gaussian mutation operator is introduced in the learning stage.Finally,the improved LSSVM prediction model is optimized with improved TLBO.Taking the measured data of Beiyan wind farm in Shanghai and Laizhou wind farm as an example,the simulation results show that with the PSO and TLBO compared to optimize LSSVM,the improved TLBO optimization LSSVM method for short-term wind power prediction has better stability and higher accuracy.
作者 程亚丽 王致杰 刘三明 江秀臣 盛戈皞 Cheng Yali;Wang Zhijie;Liu Sanming;Jiang Xiuchen;Sheng Gehao(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China;Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处 《电测与仪表》 北大核心 2019年第13期81-85,共5页 Electrical Measurement & Instrumentation
基金 国家自然科学基金资助项目(51477099) 上海市自然科学基金资助项目(15ZR1417300,14ZR1417200) 上海市教委创新基金项目(14YZ157,15ZZ106)
关键词 风电功率短期预测 改进TLBO LSSVM 自适应教学因子 高斯变异算子 short-term forecast of wind power improved TLBO LSSVM adaptive teaching factor Gaussian mutation operator
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