王玥;张宇帆;李昭昱;艾芊;吕天光;

• 1:电力传输与功率变换控制教育部重点实验室(上海交通大学)

摘要(Abstract)：

随着对利用新能源的迫切需求,分布式能源将以"能量细胞"的形式分布在用户侧,并具有即插即用的特点。受到利益的驱动或稳定性的要求,"能量细胞"将组成形态各异的"能量组织",而新形成的"能量组织"则存在历史数据较少的问题。针对"能量组织"中小样本日前负荷概率预测问题,提出基于pinball损失函数的深度长短时记忆(long short-term memory,LSTM)网络概率预测方法。为解决小样本下深度LSTM网络的过拟合问题,采用自底向上的层次聚类方法进行数据增强,并针对各个分位点进行并行预测。实验结果表明,所提方法能够获得较高的可靠性以及锐度较好的置信区间,可以为日前调度提供合理依据。

关键词(KeyWords)： 能源细胞-组织;即插即用;日前负荷概率预测;分位点;pinball损失函数;深度LSTM;数据增强

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学联合基金项目(U1866206)~~

作者(Author): 王玥;张宇帆;李昭昱;艾芊;吕天光;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.0533

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