The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical r...The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.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 t...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
Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning...Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning(LFTL)method that enables rapid and accurate forecasting of new distributed PVs.Firstly,the raw fluctuating PV data are preprocessed through decomposition to separate low-and high-frequency components.These compo-nents are then multi-scale segmented to capture diverse temporal characteristics.Following feature compression and LSTM temporal modeling,the informative features from the source domain enable lightweight transfer.For the target domain,a channel-independent encoder is designed to prevent negative interactions between het-erogeneous frequencies.The frequency-fused segment-independent decoder equipped with positional embed-dings enables local temporal analysis and reduces error accumulation of multi-step forecasts.LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity.LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.展开更多
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
文摘The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.
基金subsidized by the funds of institutional support of a long-term conceptual advancement ofscience and research number IP400040 at the Faculty of Informatics and Statistics,University of Economics,Prague,CzechRepublic.
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.
文摘Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning(LFTL)method that enables rapid and accurate forecasting of new distributed PVs.Firstly,the raw fluctuating PV data are preprocessed through decomposition to separate low-and high-frequency components.These compo-nents are then multi-scale segmented to capture diverse temporal characteristics.Following feature compression and LSTM temporal modeling,the informative features from the source domain enable lightweight transfer.For the target domain,a channel-independent encoder is designed to prevent negative interactions between het-erogeneous frequencies.The frequency-fused segment-independent decoder equipped with positional embed-dings enables local temporal analysis and reduces error accumulation of multi-step forecasts.LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity.LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.
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.
