An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited i...An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.展开更多
为了提高高速车辆-道岔复杂动力学响应的仿真计算效率和计算精度,构建了基于长短时记忆(Long Short Term Memory,LSTM)网络的动力学响应预测模型。首先,利用刚柔耦合动力学模型,生成不同水平不平顺状态下的轮轨垂向力样本。然后,将水平...为了提高高速车辆-道岔复杂动力学响应的仿真计算效率和计算精度,构建了基于长短时记忆(Long Short Term Memory,LSTM)网络的动力学响应预测模型。首先,利用刚柔耦合动力学模型,生成不同水平不平顺状态下的轮轨垂向力样本。然后,将水平不平顺幅值作为模型输入,轮轨垂向力作为输出,引入dropout参数,训练LSTM网络并采用均方误差和决定系数来评价模型性能。最后,基于计算结果分析轮重减载率和统计超限概率。结果表明:本文搭建的LSTM网络在训练轮次达到5000次后,均方误差基本稳定在0.00267,测试集的决定系数为0.903,模型具有较高的可靠性,可用于预测不平顺状态下的车辆-道岔动力学响应;与传统动力学模型计算相比,LSTM模型计算效率提高了约26倍,大幅提高了计算效率且计算精度达到要求;水平不平顺幅值达到6 mm时,超限概率达到了9.08%,超过了容许阈值。展开更多
评论是消费者对商品评价和反馈的一种文本形式。评论摘要是指对评论进行提取和压缩,形成能够概括评论信息的短文本。目前,评论摘要任务大多只关注评论的文本序列,忽略了评论中的方面、意见短语和情感极性等相关评价对象信息。因此,提出...评论是消费者对商品评价和反馈的一种文本形式。评论摘要是指对评论进行提取和压缩,形成能够概括评论信息的短文本。目前,评论摘要任务大多只关注评论的文本序列,忽略了评论中的方面、意见短语和情感极性等相关评价对象信息。因此,提出了一种基于T5模型(Text-to-Text Transfer Transformer),结合评价对象信息的评论摘要方法。该方法首先利用T5模型对评论摘要任务进行建模,通过注意力机制学习评论文本中的上下文信息,生成包含核心语义的摘要文本;然后提取摘要文本中的评价对象信息,并将其作为评论摘要任务的辅助信息;最后利用少样本数据对模型参数进行特异性调整,进一步改善摘要的效果,从而生成高质量的评论摘要。实验结果表明,在酒店评论数据集SPACE和产品评论数据集OPOSUM+上,该方法相较于基准模型在ROUGE评价指标上均有显著提升。展开更多
基金Project(51606225) supported by the National Natural Science Foundation of ChinaProject(2016JJ2144) supported by Hunan Provincial Natural Science Foundation of ChinaProject(502221703) supported by Graduate Independent Explorative Innovation Foundation of Central South University,China
文摘An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
文摘为了提高高速车辆-道岔复杂动力学响应的仿真计算效率和计算精度,构建了基于长短时记忆(Long Short Term Memory,LSTM)网络的动力学响应预测模型。首先,利用刚柔耦合动力学模型,生成不同水平不平顺状态下的轮轨垂向力样本。然后,将水平不平顺幅值作为模型输入,轮轨垂向力作为输出,引入dropout参数,训练LSTM网络并采用均方误差和决定系数来评价模型性能。最后,基于计算结果分析轮重减载率和统计超限概率。结果表明:本文搭建的LSTM网络在训练轮次达到5000次后,均方误差基本稳定在0.00267,测试集的决定系数为0.903,模型具有较高的可靠性,可用于预测不平顺状态下的车辆-道岔动力学响应;与传统动力学模型计算相比,LSTM模型计算效率提高了约26倍,大幅提高了计算效率且计算精度达到要求;水平不平顺幅值达到6 mm时,超限概率达到了9.08%,超过了容许阈值。
文摘评论是消费者对商品评价和反馈的一种文本形式。评论摘要是指对评论进行提取和压缩,形成能够概括评论信息的短文本。目前,评论摘要任务大多只关注评论的文本序列,忽略了评论中的方面、意见短语和情感极性等相关评价对象信息。因此,提出了一种基于T5模型(Text-to-Text Transfer Transformer),结合评价对象信息的评论摘要方法。该方法首先利用T5模型对评论摘要任务进行建模,通过注意力机制学习评论文本中的上下文信息,生成包含核心语义的摘要文本;然后提取摘要文本中的评价对象信息,并将其作为评论摘要任务的辅助信息;最后利用少样本数据对模型参数进行特异性调整,进一步改善摘要的效果,从而生成高质量的评论摘要。实验结果表明,在酒店评论数据集SPACE和产品评论数据集OPOSUM+上,该方法相较于基准模型在ROUGE评价指标上均有显著提升。
