基于相关性分析和长短期记忆网络分位数回归的短期公共楼宇负荷概率密度预测Short-term Public Building Load Probability Density Prediction Based on Correlation Analysis and Long-and Short-term Memory Network Quantile Regression
杨秀;陈斌超;朱兰;方陈;
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
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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