基于KELM-AdaBoost方法的短期风电功率预测（英文）  

Short-term Wind Power Forecasting Based on KELM-AdaBoost method

作　　者：李军[1] 闫佳佳 LI Jun;YAN Jia-jia(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou,730070)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《控制工程》2019年第3期492-501,共10页Control Engineering of China

基　　金：国家自然科学基金资助项目资助(51467008);光电技术与智能控制教育部重点实验室(兰州交通大学)开放课题(KFKT2016-3)

摘　　要：针对短期风电功率预测系统,提出基于集成学习理论的具有数据集实例权重更新机制的KELM-AdaBoost方法。Ada Boost方法能够自动学习多个弱回归器并将其提升为预测精度高的强回归器,核极限学习机(Kernel Extreme Learning Machine, KELM)方法作为AdaBoost方法的基学习器,其以核函数表示未知的隐含层非线性特征映射,仅需通过正则化最小二乘算法调节网络的输出权值就能达到最小的训练误差,且KELM中不仅使用了RBF核函数,还使用了可允许的多维张量积小波核函数。将KELM-AdaBoost方法分别应用于不同地区的短期风电功率单步直接预测和多步间接预测中,并与RBF,SVM, ELM, KELM, RBF-AdaBoost, SVM-AdaBoost, ELM-AdaBoost方法在同等条件下相比较,实验结果表明,所提出的KELM-AdaBoost方法在预测精度上优于已有的预测方法,蕴藏着巨大潜力和较好的应用前景。For short-term wind power forecasting, a KELM-AdaBoost method with weight update mechanism for a data set instance is proposed based on ensemble learning theory. The AdaBoost method can automatically learn multiple weak regressors and boost them into an arbitrarily accurate strong regressor, meanwhile, using kernel extreme learning machine(KELM) as the base learner of the AdaBoost method, which only adjusts the output weights of networks by using the regularization least square algorithm to achieve the minimum training error and the unknown nonlinear feature mapping of the hidden layer is represented with a kernel function, and the KELM method not only uses the RBF kernel function, but also uses the permissible multi-dimension tensor product wavelet kernel function. The proposed KELM-AdaBoost method is applied to the single-step direct forecasting of short-term wind power and the multi-step indirect forecasting in different regions respectively, and the validity of the KELM-AdaBoost method is verified by comparing its accuracy with RBF, SVM, ELM, KELM, RBF-AdaBoost, SVM-AdaBoost, ELM-AdaBoost methods under the same condition, the experiment results show that the proposed KELM-AdaBoost method is superior to the existing forecasting methods on the forecasting accuracy, therefore, it contains a huge potential and good application prospect.

关 键 词：ADABOOST 核极限学习机 风电功率 预测 

分 类 号：TM614[电气工程—电力系统及自动化]