This study presents a framework for forecasting electricity demand in port microgrids using advanced machine learning models,including Random Forest,Least Squares Boosting Ensemble,and Gaussian Process Regression.Thes...This study presents a framework for forecasting electricity demand in port microgrids using advanced machine learning models,including Random Forest,Least Squares Boosting Ensemble,and Gaussian Process Regression.These models were evaluated under different forecasting setups(fixed origin,expanding windows,and rolling windows)and compared against simpler baseline methods,such as Linear Regression and Naive models.The study assessed the effectiveness of machine learning models in handling dynamic electricity demand patterns in port environments and highlighted the advantages of data-driven models.Results indicate that the Random Forest(expanding window)model outperforms the other models,achieving a root mean square error of 1.1848 MW and a mean average percentage error of 7.2483%.Gaussian Process Regression with Exponential kernel follows closely with a root mean square error of 1.1904 MW and a mean average percentage error of 7.5017%.In contrast,the Naive Method(previous day)shows the poorest performance with a root mean square error of 4.5357 MW and a mean average percentage error of 18.1485%.Partial Dependence Plots reveal that features such as weighted port calls play a significant role in improving prediction accuracy.These findings highlight the effectiveness of machine learning models in accurately forecasting port microgrid demand and optimizing energy management.展开更多
Since the recent economic crisis, the undervaluation of China＇s exchange rate has been a focus in the debate on the global policy mix. Using a non-competitive input-output table, we establish a comparative-static gen...Since the recent economic crisis, the undervaluation of China＇s exchange rate has been a focus in the debate on the global policy mix. Using a non-competitive input-output table, we establish a comparative-static general equilibrium model to simulate the impact of real exchange rate changes on Sino-US trade and labor markets. The simulation shows that the impacts of a lO-percent RMB revaluation on the trade surplus of China and the labor market of the USA are more modest than is generally perceived, and the negative impact on the output of the non-processing industry in China is more significant than that on the processing industry. The Sino--US trade imbalance will continue to deteriorate, China ＇s non-processing trade surplus will decline and the processing trade will increase, with the combined effect being small. For the USA, labor-intensive goods imported from China will shift to different Asian countries instead of transferring back to the US market. The simulation results indicate that the impacts of an RMB revaluation on both Chinese and US labor markets would be limited.展开更多
The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performa...The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performance was investigated in a fixed-bed system with respect to the adsorption superficial velocity,ionic strength and pH.A mathematical model was used to simulate the mass transfer mechanism,taking film mass transfer,pore diffusion and axial dispersion into account.The model predictions were consistent with the experi-mental data and were consequently used to determine the mass transfer coefficients.展开更多
文摘This study presents a framework for forecasting electricity demand in port microgrids using advanced machine learning models,including Random Forest,Least Squares Boosting Ensemble,and Gaussian Process Regression.These models were evaluated under different forecasting setups(fixed origin,expanding windows,and rolling windows)and compared against simpler baseline methods,such as Linear Regression and Naive models.The study assessed the effectiveness of machine learning models in handling dynamic electricity demand patterns in port environments and highlighted the advantages of data-driven models.Results indicate that the Random Forest(expanding window)model outperforms the other models,achieving a root mean square error of 1.1848 MW and a mean average percentage error of 7.2483%.Gaussian Process Regression with Exponential kernel follows closely with a root mean square error of 1.1904 MW and a mean average percentage error of 7.5017%.In contrast,the Naive Method(previous day)shows the poorest performance with a root mean square error of 4.5357 MW and a mean average percentage error of 18.1485%.Partial Dependence Plots reveal that features such as weighted port calls play a significant role in improving prediction accuracy.These findings highlight the effectiveness of machine learning models in accurately forecasting port microgrid demand and optimizing energy management.
基金supported by the National Natural Science Foundation of China(Grant No.70810107020)the Science Foundation of Ministfy of Education of China(Grant No.2009JJD790002)the China Postdoctoral Science Foundation (No.20100470125)
文摘Since the recent economic crisis, the undervaluation of China＇s exchange rate has been a focus in the debate on the global policy mix. Using a non-competitive input-output table, we establish a comparative-static general equilibrium model to simulate the impact of real exchange rate changes on Sino-US trade and labor markets. The simulation shows that the impacts of a lO-percent RMB revaluation on the trade surplus of China and the labor market of the USA are more modest than is generally perceived, and the negative impact on the output of the non-processing industry in China is more significant than that on the processing industry. The Sino--US trade imbalance will continue to deteriorate, China ＇s non-processing trade surplus will decline and the processing trade will increase, with the combined effect being small. For the USA, labor-intensive goods imported from China will shift to different Asian countries instead of transferring back to the US market. The simulation results indicate that the impacts of an RMB revaluation on both Chinese and US labor markets would be limited.
文摘The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performance was investigated in a fixed-bed system with respect to the adsorption superficial velocity,ionic strength and pH.A mathematical model was used to simulate the mass transfer mechanism,taking film mass transfer,pore diffusion and axial dispersion into account.The model predictions were consistent with the experi-mental data and were consequently used to determine the mass transfer coefficients.
