An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the joint...An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the jointing stage(July,2017)and extracting five vegetation indices.Vegetation indices and rice growth parameter data were compared and analyzed.Effective predictors were screened by using significance analysis and quantile and ordinary least square(OLS)regression models estimating rice yields were constructed.The results showed that a quantile regression model based on normalized difference vegetation indices(NDVI)and rice yields performed was best forτ=0.7 quantile.Thus,NDVI was determined as an effective variable for the rice yield estimation during the jointing stage.The accuracy of the quantile regression estimation model was then assessed using RMES and MAPE test indicators.The yields by this approach had better results than those of an OLS regression estimation model and showed that quantile regression had practical applications and research significance in rice yields estimation.展开更多
为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量...为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量和降雨量将历史数据集划分为晴天、多云天和阴雨天3种场景,生成具有相似天气类型的测试集和训练样本集:然后,应用TCN进行集成特征维度提取,利用BiLSTM神经网络建模进行输出功率和天气数据时间序列的双向拟合.针对传统区间预测分位数损失函数不可微的缺陷,引入Huber范数近似替代原损失函数,并应用梯度下降进行优化,构建改进的可微分位数回归(quantile regression,简称QR)模型,生成置信区间.最后,采用核密度估计(kerneldensity estimation,简称KDE)给出概率密度预测结果。以我国华东某地区分布式光伏电站作为研究对象,与现有概率预测方法相比,该文所提出的短期预测算法的功率区间各评价指标都有所改进,验证了所提方法的可靠性。展开更多
在文献中,分位点回归模型是线性的,但是在实际中,这个假设不能很好地满足需要.为此提出了分位点回归的门限模型,用该模型实证分析了单只股票(浦东发展银行)的条件 VaR.选择了一种流动性风险指标作为条件,因此该条件 VaR 也可以看作是流...在文献中,分位点回归模型是线性的,但是在实际中,这个假设不能很好地满足需要.为此提出了分位点回归的门限模型,用该模型实证分析了单只股票(浦东发展银行)的条件 VaR.选择了一种流动性风险指标作为条件,因此该条件 VaR 也可以看作是流动性调整的 VaR(La-VaR).经过实证分析发现,由门限分位点模型得到的结果能够更好地描述实际市场情况,也能更好地预测市场风险.展开更多
基金Supported by the National Key R&D Program of China(2016YFD020060305)。
文摘An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the jointing stage(July,2017)and extracting five vegetation indices.Vegetation indices and rice growth parameter data were compared and analyzed.Effective predictors were screened by using significance analysis and quantile and ordinary least square(OLS)regression models estimating rice yields were constructed.The results showed that a quantile regression model based on normalized difference vegetation indices(NDVI)and rice yields performed was best forτ=0.7 quantile.Thus,NDVI was determined as an effective variable for the rice yield estimation during the jointing stage.The accuracy of the quantile regression estimation model was then assessed using RMES and MAPE test indicators.The yields by this approach had better results than those of an OLS regression estimation model and showed that quantile regression had practical applications and research significance in rice yields estimation.
文摘为更好地描述光伏出力不确定性,该文提出了一种基于时序卷积网络(temporal convolutional network,简称TCN)和双向长短期记忆(bidirectional long short term memory,简称BiLSTM)的光伏功率概率预测模型.首先,基于数值天气预报中的云量和降雨量将历史数据集划分为晴天、多云天和阴雨天3种场景,生成具有相似天气类型的测试集和训练样本集:然后,应用TCN进行集成特征维度提取,利用BiLSTM神经网络建模进行输出功率和天气数据时间序列的双向拟合.针对传统区间预测分位数损失函数不可微的缺陷,引入Huber范数近似替代原损失函数,并应用梯度下降进行优化,构建改进的可微分位数回归(quantile regression,简称QR)模型,生成置信区间.最后,采用核密度估计(kerneldensity estimation,简称KDE)给出概率密度预测结果。以我国华东某地区分布式光伏电站作为研究对象,与现有概率预测方法相比,该文所提出的短期预测算法的功率区间各评价指标都有所改进,验证了所提方法的可靠性。
文摘在文献中,分位点回归模型是线性的,但是在实际中,这个假设不能很好地满足需要.为此提出了分位点回归的门限模型,用该模型实证分析了单只股票(浦东发展银行)的条件 VaR.选择了一种流动性风险指标作为条件,因此该条件 VaR 也可以看作是流动性调整的 VaR(La-VaR).经过实证分析发现,由门限分位点模型得到的结果能够更好地描述实际市场情况,也能更好地预测市场风险.
