由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimizati...由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimization,TO)和变分模态分解结合长短期记忆网络(variational mode decomposition based long short-term memory network,VMD-LSTM)的多元负荷预测方法。首先,使用斯皮尔曼等级(Spearman rank,SR)相关系数定量计算多元负荷间以及负荷与其他气候因素间的相关关系并通过循环寻优确定最优相关阈值,然后采用VMD算法将以最优阈值筛选出的负荷特征序列分解成更简单、平稳、有规律性的本征模态函数(intrinsic mode function,IMF)后与最优气象特征一起输入LSTM模型进行负荷预测。通过某用户级IES的实际数据对所提方法的有效性进行了验证,结果表明,所提方法能有效提高IES的多元负荷预测精度。展开更多
[Objective] The aim was to al eviate the constructed wetland clogging problems and to explore to the effects of hydraulic loading on wetland clogging. [Method] The experiment, through artificial soil columns, simulate...[Objective] The aim was to al eviate the constructed wetland clogging problems and to explore to the effects of hydraulic loading on wetland clogging. [Method] The experiment, through artificial soil columns, simulated vertical flow arti-ficial wetland, set four hydraulic load level at 50, 100, 150 and 200 cm/d, to identify the impact of hydraulic loading on wetland clogging and to explore the factors run-ning threshold. [Result] The results show that the different levels of hydraulic loading have greater impact; in the constructed wetland clogging process under high hy-draulic loading of 200 cm/d, the effective life was only six months, and the single factor can be speculated that the threshold of the hydraulic load should be at 100-150 cm/d; system can last for six months at low hydraulic loading and last for three months at medium hydraulic load. [Conclusion] The research provides references for wetland clogging experiments in future.展开更多
针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数...针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.展开更多
文摘由于用户级综合能源系统(integrated energy system,IES)的多元负荷序列之间复杂的耦合关系及易受外部因素影响等原因,综合能源系统多元负荷的精准预测面临很大困难。为此,提出一种基于Spearman相关性分析阈值寻优(threshold optimization,TO)和变分模态分解结合长短期记忆网络(variational mode decomposition based long short-term memory network,VMD-LSTM)的多元负荷预测方法。首先,使用斯皮尔曼等级(Spearman rank,SR)相关系数定量计算多元负荷间以及负荷与其他气候因素间的相关关系并通过循环寻优确定最优相关阈值,然后采用VMD算法将以最优阈值筛选出的负荷特征序列分解成更简单、平稳、有规律性的本征模态函数(intrinsic mode function,IMF)后与最优气象特征一起输入LSTM模型进行负荷预测。通过某用户级IES的实际数据对所提方法的有效性进行了验证,结果表明,所提方法能有效提高IES的多元负荷预测精度。
基金Supported by National Natural Science Foundation of China(41071214)~~
文摘[Objective] The aim was to al eviate the constructed wetland clogging problems and to explore to the effects of hydraulic loading on wetland clogging. [Method] The experiment, through artificial soil columns, simulated vertical flow arti-ficial wetland, set four hydraulic load level at 50, 100, 150 and 200 cm/d, to identify the impact of hydraulic loading on wetland clogging and to explore the factors run-ning threshold. [Result] The results show that the different levels of hydraulic loading have greater impact; in the constructed wetland clogging process under high hy-draulic loading of 200 cm/d, the effective life was only six months, and the single factor can be speculated that the threshold of the hydraulic load should be at 100-150 cm/d; system can last for six months at low hydraulic loading and last for three months at medium hydraulic load. [Conclusion] The research provides references for wetland clogging experiments in future.
文摘针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.