杨秀;陈斌超;朱兰;方陈;

• 1:上海电力大学电气工程学院
• 2:上海市电力公司电力科学研究院

摘要(Abstract)：

公共楼宇是智能电网用电环节需求响应的重要组成部分,在强不确定性环境下,为了提高公共楼宇短期负荷预测的精度,并能更好反映楼宇负荷的不确定性。提出了一种集合多维尺度分析技术(multidimensional scaling,MDS),基于Copula函数相关性测度、长短期记忆网络分位数回归(quantile regression long short-term memory,QRLSTM)和核密度估计(kernel density estimation,KDE)的短期公共楼宇负荷概率密度预测的方法。首先采用MDS技术对楼宇群进行初步划分,再通过基于Copula函数的相关性测度方法定量计算影响因素(外界天气、人类活动)与目标楼宇负荷的相关程度;其次,运用QRLSTM回归模型预测未来不同分位数上的负荷值。最后,通过核密度估计得到未来任意时刻预测点的概率密度函数。实验结果表明,综合考虑强相关影响因素,并结合QRLSTM回归和KDE技术,能够更好地解决短期公共楼宇负荷概率密度预测问题。

关键词(KeyWords)： 楼宇负荷概率预测;强相关因素;多维尺度分析;Copula函数;长短期记忆网络分位数回归;核密度估计

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51807114)~~

作者(Author): 杨秀;陈斌超;朱兰;方陈;

Email:

DOI: 10.13335/j.1000-3673.pst.2018.2878

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