现有分位点回归方法在进行多分位点预测时往往需要为每个分位点单独建立模型,不仅训练成本高还会导致"分位点交叉"。对此,提出了一种基于藤copula分位数回归的光伏功率日前概率预测模型。利用藤copula对光伏功率及其条件变量...现有分位点回归方法在进行多分位点预测时往往需要为每个分位点单独建立模型,不仅训练成本高还会导致"分位点交叉"。对此,提出了一种基于藤copula分位数回归的光伏功率日前概率预测模型。利用藤copula对光伏功率及其条件变量间的相依结构进行解析化表达,基于优化算法对藤copula结构及参数进行优化,在此基础上建立起光伏功率条件分位数回归模型;在条件变量中引入光伏功率点预测量,并借助最小化连续秩概率分数(continuousrank probability score,CRPS)权衡可靠性与锐度,筛选出最佳条件组合。算例仿真结果表明,该方法克服了现有分位数回归方法的缺点,进一步提升了光伏功率概率预测性能。展开更多
This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the qu...This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the quantile of the non-terminal event time. Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecified marginal distributions. The technique of inverse probability weighting (IPW) is adopted to adjust for the selection bias. Large-sample properties of the proposed estimator are derived and a model diagnostic procedure is developed to check the adequacy of the model assumption. Simulation results show that the proposed estimator performs well. For illustrative purposes, our method is applied to analyze the bone marrow transplant data in [1].展开更多
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...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。展开更多
文摘现有分位点回归方法在进行多分位点预测时往往需要为每个分位点单独建立模型,不仅训练成本高还会导致"分位点交叉"。对此,提出了一种基于藤copula分位数回归的光伏功率日前概率预测模型。利用藤copula对光伏功率及其条件变量间的相依结构进行解析化表达,基于优化算法对藤copula结构及参数进行优化,在此基础上建立起光伏功率条件分位数回归模型;在条件变量中引入光伏功率点预测量,并借助最小化连续秩概率分数(continuousrank probability score,CRPS)权衡可靠性与锐度,筛选出最佳条件组合。算例仿真结果表明,该方法克服了现有分位数回归方法的缺点,进一步提升了光伏功率概率预测性能。
文摘This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the quantile of the non-terminal event time. Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecified marginal distributions. The technique of inverse probability weighting (IPW) is adopted to adjust for the selection bias. Large-sample properties of the proposed estimator are derived and a model diagnostic procedure is developed to check the adequacy of the model assumption. Simulation results show that the proposed estimator performs well. For illustrative purposes, our method is applied to analyze the bone marrow transplant data in [1].
文摘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。
