李国庆;刘钊;金国彬;权然;

• 1:现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学)

摘要(Abstract)：

电力系统超短期负荷预测易受到气象、假日等多种因素共同作用的影响,因此,实现其精准预测较为困难。为提高预测精度,往往需要大量的历史数据进行训练。针对历史数据较少的新建初期电力系统,提出了一种基于随机分布式嵌入框架及BP神经网络的超短期电力负荷预测方法。首先,将电力系统中电力负荷变量、气象变量等各种状态变量的延迟变量视为独立的影响因素,采用BP神经网络算法针对不同组延迟变量分别进行训练和预测,得到多个预测值。然后,采用核密度估计法拟合多个预测值形成分布的概率密度函数。最后,通过期望估计法或聚合估计法计算得出电力负荷的最终预测值。选取实际负荷数据进行算例分析,结果表明,所提方法适用于训练数据较少的超短期负荷预测,且相较于几种常规预测算法具有更高的预测精度以及较强的稳定性。

关键词(KeyWords)： 超短期负荷预测;随机分布式嵌入框架;BP神经网络;非线性动力系统;短期数据

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划项目(2018YFB0904700)~~

作者(Author): 李国庆;刘钊;金国彬;权然;

Email:

DOI:

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