An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin...An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.展开更多
精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and e...精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and external perspectives on the topological dendrogram of accident causes,IEPTDAC)。首先,基于树状结构刻画事故内部诱因的拓扑关系,从“人、机、环、管”4个方面提取事故诱因的外部特征;在此基础上,采用卷积神经网络的多层卷积操作提取事故诱因的内外部特征,并引入粒子群算法对卷积神经网络的关键超参数进行优化,进一步提升模型的预测性能;最后,选取某铁路局的5个区段,以19个事故诱因与风险事故数据作为研究对象,在1、3和5 h的时间粒度下,分别采用9种既有预测模型与IEPTDAC模型进行对比分析。实验结果表明,相较于现有的组合预测模型以及传统的单一预测模型,IEPTDAC模型拥有更优的预测精度和拟合效果。例如,在1 h时间粒度下,对比实验中基于暂态提取变换与DSRNet-AttBiLSTM的预测模型,IEPTDAC模型的平均绝对误差fmae降低了32.04%,均方根误差f_(rmse)降低了36.35%,决定系数f_(r^(2))提高了0.46%;在1、3和5 h的时间粒度下,IEPTDAC与传统的ConvLSTM(convolutional long short-term memory)模型相比,f_(r^(2))分别提高1.71%、3.00%、1.27%。此外,本文设计的模型消融实验验证了IEPTDAC模型各分支的合理性和有效性。该方法为高速铁路风险预测提供了一种有效的技术手段。展开更多
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.
文摘精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and external perspectives on the topological dendrogram of accident causes,IEPTDAC)。首先,基于树状结构刻画事故内部诱因的拓扑关系,从“人、机、环、管”4个方面提取事故诱因的外部特征;在此基础上,采用卷积神经网络的多层卷积操作提取事故诱因的内外部特征,并引入粒子群算法对卷积神经网络的关键超参数进行优化,进一步提升模型的预测性能;最后,选取某铁路局的5个区段,以19个事故诱因与风险事故数据作为研究对象,在1、3和5 h的时间粒度下,分别采用9种既有预测模型与IEPTDAC模型进行对比分析。实验结果表明,相较于现有的组合预测模型以及传统的单一预测模型,IEPTDAC模型拥有更优的预测精度和拟合效果。例如,在1 h时间粒度下,对比实验中基于暂态提取变换与DSRNet-AttBiLSTM的预测模型,IEPTDAC模型的平均绝对误差fmae降低了32.04%,均方根误差f_(rmse)降低了36.35%,决定系数f_(r^(2))提高了0.46%;在1、3和5 h的时间粒度下,IEPTDAC与传统的ConvLSTM(convolutional long short-term memory)模型相比,f_(r^(2))分别提高1.71%、3.00%、1.27%。此外,本文设计的模型消融实验验证了IEPTDAC模型各分支的合理性和有效性。该方法为高速铁路风险预测提供了一种有效的技术手段。
