This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a t...This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.展开更多
以欧洲中期天气预报中心ECMWF(European Centre for Medium Range Weather Forecasts)的ERA5风场数据为真实风速参考值,利用HY-2B卫星散射计L2A数据,使用反向传播神经网络方法对风速进行了反演,分别建立了中高风速、中低风速和全风速反...以欧洲中期天气预报中心ECMWF(European Centre for Medium Range Weather Forecasts)的ERA5风场数据为真实风速参考值,利用HY-2B卫星散射计L2A数据,使用反向传播神经网络方法对风速进行了反演,分别建立了中高风速、中低风速和全风速反演模型。与基于NSCAT-4地球物理模式函数得到的L2B风速相比,在训练集中,3种网络模型反演风速的均方误差(Mean Square Error,MSE)分别达到了0.18,0.14和0.32 m/s,平均绝对值误差(Mean Absolute Error,MAE)分别达到了0.27,0.24和0.34 m/s;在测试集中,3种网络模型反演风速的均方误差(MSE)分别达到了0.54,0.27和0.46 m/s,平均绝对值误差(MAE)分别达到了0.48,0.35和0.42 m/s。研究结果表明,中高和中低风速模型优于全风速模型,其中中低风速模型反演风速的MSE和MAE最低,中高风速模型反演风速的MSE和MAE下降幅度最大;3种模型都具有良好的泛化能力。展开更多
In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the increment...In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.展开更多
研究利用神经网络方法处理微波散射计数据,反演海面风场。重点研究海洋二号(HY-2)卫星微波散射计数据反演,特别是中高风速条件下的风场反演。其中风速的反演基于后向传播(Back Propagation,BP)神经网络;多解风向的反演基于混合密度(Mixt...研究利用神经网络方法处理微波散射计数据,反演海面风场。重点研究海洋二号(HY-2)卫星微波散射计数据反演,特别是中高风速条件下的风场反演。其中风速的反演基于后向传播(Back Propagation,BP)神经网络;多解风向的反演基于混合密度(Mixture Density Network,MDN)神经网络,求解过程中的核函数采用高斯分布;网络训练的目标风场采用欧洲中期天气预报中心(European Centre for Medium-range Weather Foresting,ECMWF)模式风场。通过与ECMWF风场的比较,利用神经网络方法反演的风场可以满足HY-2微波散射计风场反演的精度要求。同时通过与国家卫星海洋应用中心发布的HY-2微波散射计L2B级风场产品相比较,表明该方法反演的风场更接近ECMWF模式风场。展开更多
基金supported by the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(No.U19A20106)the Science and Technology Major Projects of Anhui Province(No.202203f07020003)the Science and Technology Project of State Grid Corporation of China(No.52120522000F).
文摘This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.
文摘以欧洲中期天气预报中心ECMWF(European Centre for Medium Range Weather Forecasts)的ERA5风场数据为真实风速参考值,利用HY-2B卫星散射计L2A数据,使用反向传播神经网络方法对风速进行了反演,分别建立了中高风速、中低风速和全风速反演模型。与基于NSCAT-4地球物理模式函数得到的L2B风速相比,在训练集中,3种网络模型反演风速的均方误差(Mean Square Error,MSE)分别达到了0.18,0.14和0.32 m/s,平均绝对值误差(Mean Absolute Error,MAE)分别达到了0.27,0.24和0.34 m/s;在测试集中,3种网络模型反演风速的均方误差(MSE)分别达到了0.54,0.27和0.46 m/s,平均绝对值误差(MAE)分别达到了0.48,0.35和0.42 m/s。研究结果表明,中高和中低风速模型优于全风速模型,其中中低风速模型反演风速的MSE和MAE最低,中高风速模型反演风速的MSE和MAE下降幅度最大;3种模型都具有良好的泛化能力。
文摘In large-scale data centers, many servers are in- terconnected via a dedicated networking structure, so as to satisfy specific design goals, such as the low equipment cost, the high network capacity, and the incremental expansion. The topological properties of a networking structure are criti- cal factors that dominate the performance of the entire data center. The existing networking structures are either fully random or completely structured. Although such networking structures exhibit advantages on given aspects, they suffer ob- vious shortcomings in other essential fields. In this paper, we aim to design a hybrid topology, called R3, which is the com- pound graph of structured and random topology. It employs random regular graph as a unit duster and connects many such clusters by means of a structured topology, i.e., the gen- eralized hypercube. Consequently, the hybrid topology com- bines the advantages of structured as well as random topolo- gies seamlessly. Meanwhile, a coloring-based algorithm is proposed for R3 to enable fast and accurate routing. R3 pos- sesses many attractive characteristics, such as the modularity and expansibility at the cost of only increasing the degree of any node by one. Comprehensive evaluation results show that our hybrid topology possesses excellent topology properties and network performance.
文摘研究利用神经网络方法处理微波散射计数据,反演海面风场。重点研究海洋二号(HY-2)卫星微波散射计数据反演,特别是中高风速条件下的风场反演。其中风速的反演基于后向传播(Back Propagation,BP)神经网络;多解风向的反演基于混合密度(Mixture Density Network,MDN)神经网络,求解过程中的核函数采用高斯分布;网络训练的目标风场采用欧洲中期天气预报中心(European Centre for Medium-range Weather Foresting,ECMWF)模式风场。通过与ECMWF风场的比较,利用神经网络方法反演的风场可以满足HY-2微波散射计风场反演的精度要求。同时通过与国家卫星海洋应用中心发布的HY-2微波散射计L2B级风场产品相比较,表明该方法反演的风场更接近ECMWF模式风场。
