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Application of support vector machine model in load forecasting based on adaptive particle swarm optimization
Author(s): 
Pages: 43-46,51
Year: Issue:  15
Journal: Relay

Abstract: 为了提高电力系统短期负荷预测精度,针对传统支持向量机(SVM)模型在负荷预测中存在的参数的选取问题,提出一种新的预测模型:用改进的自适应粒子群优化算法寻求SVM模型的最优参数.经典粒子群算法是一种全局优化算法,在此基础上提出改进的粒子群算法(FAPSO)并对其进行收敛性测试,将基于改进粒子群算法FAPSO优化的SVM模型用于短期电力负荷预测,实例仿真结果表明该预测模型精度高于传统SVM预测模型,具...
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