为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and l...为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short term memory network,CNNLSTM)混合预测算法。该算法首先通过经验模态分解法将负荷数据分解为平稳性好、规律性强的若干本征模态函数(intrinsic mode functions,IMF)和残差(residual,Res)。其次为了简化后续模型的计算体量,运用k均值聚类方法对分解所得的各分量进行分组集成,同时分析不同聚类数对应的预测效果,选取最优聚类标签构造神经网络输入数据。之后将各组数据分别输入到CNN-LSTM混合神经网络中,利用CNN挖掘数据间的特征形成特征向量,并将其输入到LSTM中进行预测。最后将所有预测结果进行线性相加得到完整预测负荷。通过在真实负荷上进行验证并与现有模型进行比较,所提方法具有更高的预测精度。展开更多
The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning technique...The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks （CNNs）, part detection based, ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively.展开更多
文摘为了减少复杂环境因素对电力负荷超短期预测效果的影响,提高算法的预测精度和运算效率,该文提出一种基于聚类经验模态分解(clusterempiricalmodedecomposition,CEMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short term memory network,CNNLSTM)混合预测算法。该算法首先通过经验模态分解法将负荷数据分解为平稳性好、规律性强的若干本征模态函数(intrinsic mode functions,IMF)和残差(residual,Res)。其次为了简化后续模型的计算体量,运用k均值聚类方法对分解所得的各分量进行分组集成,同时分析不同聚类数对应的预测效果,选取最优聚类标签构造神经网络输入数据。之后将各组数据分别输入到CNN-LSTM混合神经网络中,利用CNN挖掘数据间的特征形成特征向量,并将其输入到LSTM中进行预测。最后将所有预测结果进行线性相加得到完整预测负荷。通过在真实负荷上进行验证并与现有模型进行比较,所提方法具有更高的预测精度。
基金supported by the National Natural Science Foundation of China(Nos.61373121 and 61328205)Program for Sichuan Provincial Science Fund for Distinguished Young Scholars(No.13QNJJ0149)+1 种基金the Fundamental Research Funds for the Central UniversitiesChina Scholarship Council(No.201507000032)
文摘The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks （CNNs）, part detection based, ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively.
