Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smar...Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smart grids,and ultimately support construction of smart energy cities.However,different from centralized PV power forecasts,three critical challenges are encountered in distributed PV power forecasting:1)lack of on-site meteorological observation,2)leveraging extraneous data to enhance forecasting performance,3)spatial-temporal modelling methods of meteorological information around the distributed PV stations.To address these issues,we propose a Graph Spatial-Temporal Attention Neural Network(GSTANN)to predict the very short-term power of distributed PV.First,we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations.Then,we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations.Subsequently,we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power.Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.展开更多
This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation ...This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation to the onset of the global economic recession. The different behavior of the wage distribution of Czech and Slovak employees during the period is the subject of research. The article discusses the differences in the wage level between men and women in the Czech and Slovak Republics. There are the total wage distributions of men and women together, both in the Czech Republic and in the Slovak Republic on one hand, and wage distributions according to the gender separately for men and women on the other hand. Special attention was paid to the development of Gini coefficient of the concentration in both countries according to the gender in the period under review, too.展开更多
基金supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA27000000)。
文摘Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smart grids,and ultimately support construction of smart energy cities.However,different from centralized PV power forecasts,three critical challenges are encountered in distributed PV power forecasting:1)lack of on-site meteorological observation,2)leveraging extraneous data to enhance forecasting performance,3)spatial-temporal modelling methods of meteorological information around the distributed PV stations.To address these issues,we propose a Graph Spatial-Temporal Attention Neural Network(GSTANN)to predict the very short-term power of distributed PV.First,we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations.Then,we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations.Subsequently,we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power.Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.
文摘This paper deals with the development of wage distribution by gender in the Czech and Slovak Republics in the years of 2005-2012. Special attention is given to changes in the behavior of wage distribution in relation to the onset of the global economic recession. The different behavior of the wage distribution of Czech and Slovak employees during the period is the subject of research. The article discusses the differences in the wage level between men and women in the Czech and Slovak Republics. There are the total wage distributions of men and women together, both in the Czech Republic and in the Slovak Republic on one hand, and wage distributions according to the gender separately for men and women on the other hand. Special attention was paid to the development of Gini coefficient of the concentration in both countries according to the gender in the period under review, too.