耿艳;韩学山;韩力;

• 1:山东大学电气工程学院

摘要(Abstract)：

提出了结合粗糙集(rough sets,RS)理论和遗传算法(genetic algorithm,GA)的最小二乘支持向量机(least squares support vector machines,LS-SVM)短期负荷预测模型和算法。由于影响负荷预测精度的因素众多,该模型采用RS理论进行历史数据的预处理,对各条件属性进行约简分析。属性约简采用GA进行寻优,以确定与负荷密切相关的因素,作为LS-SVM的有效输入变量。在预测过程中,通过GA对LS-SVM的模型参数进行自适应寻优,从而提高负荷预测精度,避免LS-SVM对经验的依赖以及预测过程中对模型参数的盲目选择。采用上述方法对山东电网负荷进行了预测分析,结果证明了该方法的有效性。

关键词(KeyWords)： 电力系统;短期负荷预测;支持向量机;粗糙集;遗传算法

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(50377021,50677036)~~

作者(Author): 耿艳;韩学山;韩力;

Email:

DOI:

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