风电场准确的风速预测可以减轻或避免风电对电网的不利影响,有利于在开放的电力市场环境下正确制定电能交换计划,提高风电竞争力。基于风速序列的时序性,使用极大似然法对风速序列进行了Box-Cox最优变换,建立了ARMA(p,q)风速预测模型。...风电场准确的风速预测可以减轻或避免风电对电网的不利影响,有利于在开放的电力市场环境下正确制定电能交换计划,提高风电竞争力。基于风速序列的时序性,使用极大似然法对风速序列进行了Box-Cox最优变换,建立了ARMA(p,q)风速预测模型。为检验时间序列模型的有效性,利用最小信息准则中的BIC(Bayesian Information Criterion)函数对ARMA(p,q)模型进行识别,并通过风速频率曲线对预测结果进行了修正。仿真结果和算例验证了该方法在风电场风速预测中的适用性,具有一定的实用价值。展开更多
This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are ...This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate.The authors show that under some appropriate conditions,the SCAD-penalized least squares estimator has the so called "oracle property".In addition,the authors also suggest a BIC criterion to select the tuning parameter,and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models.Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.展开更多
文摘风电场准确的风速预测可以减轻或避免风电对电网的不利影响,有利于在开放的电力市场环境下正确制定电能交换计划,提高风电竞争力。基于风速序列的时序性,使用极大似然法对风速序列进行了Box-Cox最优变换,建立了ARMA(p,q)风速预测模型。为检验时间序列模型的有效性,利用最小信息准则中的BIC(Bayesian Information Criterion)函数对ARMA(p,q)模型进行识别,并通过风速频率曲线对预测结果进行了修正。仿真结果和算例验证了该方法在风电场风速预测中的适用性,具有一定的实用价值。
基金supported by the National Natural Science Foundation of China under Grant Nos.11471029,11101014,61273221 and 11171010the Beijing Natural Science Foundation under Grant Nos.1142002 and 1112001+1 种基金the Science and Technology Project of Beijing Municipal Education Commission under Grant No.KM201410005010the Research Fund for the Doctoral Program of Beijing University of Technology under Grant No.006000543114550
文摘This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models.The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate.The authors show that under some appropriate conditions,the SCAD-penalized least squares estimator has the so called "oracle property".In addition,the authors also suggest a BIC criterion to select the tuning parameter,and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models.Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.
