As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy....As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.展开更多
The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM)...The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia.Two other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for comparison.This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data.The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and precipitation.Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations.The performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River Basin.Compared to classic machine learning algorithms,LSTM shows significant advantages over most evaluating indices.XGBoost also has high NS value in the training period,but is prone to overfitting the discharge.Compared with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data inputs.Moreover,LSTM only requires meteorological data rather than physical characteristics of underlying data.As an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at the cost of increasing the complexity of the model.Compared with the oftentimes complex semi-distributed physical hydrological models,the LSTM method not only eliminates the tedious calibration process of hydrological parameters,but also significantly reduces the calculation time and costs.Overall,LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.展开更多
基金Supported by the National Science and Technology Basic Work Project of China Meteorological Administration(2005DKA31700-06)Innovation Fund of Public Meteorological Service Center of China Meteorological Administration(M2020013)。
文摘As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.
基金supported by the National Natural Science Foundation of China(U1903208,41630859,42071046)。
文摘The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia.Two other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for comparison.This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data.The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and precipitation.Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations.The performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River Basin.Compared to classic machine learning algorithms,LSTM shows significant advantages over most evaluating indices.XGBoost also has high NS value in the training period,but is prone to overfitting the discharge.Compared with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data inputs.Moreover,LSTM only requires meteorological data rather than physical characteristics of underlying data.As an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at the cost of increasing the complexity of the model.Compared with the oftentimes complex semi-distributed physical hydrological models,the LSTM method not only eliminates the tedious calibration process of hydrological parameters,but also significantly reduces the calculation time and costs.Overall,LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.
