A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series...A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.展开更多
We study the enhancement of accuracy,by means of the convolution postprocessing technique,for discontinuous Galerkin(DG)approximations to hyperbolic problems.Previous investigations have focused on the superconvergenc...We study the enhancement of accuracy,by means of the convolution postprocessing technique,for discontinuous Galerkin(DG)approximations to hyperbolic problems.Previous investigations have focused on the superconvergence obtained by this technique for elliptic,time-dependent hyperbolic and convection-diffusion problems.In this paper,we demonstrate that it is possible to extend this postprocessing technique to the hyperbolic problems written as the Friedrichs’systems by using an upwind-like DG method.We prove that the L2-error of the DG solution is of order k+1/2,and further the post-processed DG solution is of order 2k+1 if Qkpolynomials are used.The key element of our analysis is to derive the(2k+1)-order negative norm error estimate.Numerical experiments are provided to illustrate the theoretical analysis.展开更多
基金supported in part by Special Fund of the National Basic Research Program of China(2013CB228204)NSFCNRCT Collaborative Project(No.51561145011)+1 种基金Australian Research Council Project(DP120101345)State Grid Corporation of China.
文摘A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.
基金This work was supported by the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds 2013ZCX02the National Natural Science Funds of China 11371081
文摘We study the enhancement of accuracy,by means of the convolution postprocessing technique,for discontinuous Galerkin(DG)approximations to hyperbolic problems.Previous investigations have focused on the superconvergence obtained by this technique for elliptic,time-dependent hyperbolic and convection-diffusion problems.In this paper,we demonstrate that it is possible to extend this postprocessing technique to the hyperbolic problems written as the Friedrichs’systems by using an upwind-like DG method.We prove that the L2-error of the DG solution is of order k+1/2,and further the post-processed DG solution is of order 2k+1 if Qkpolynomials are used.The key element of our analysis is to derive the(2k+1)-order negative norm error estimate.Numerical experiments are provided to illustrate the theoretical analysis.
