深圳大学管理学院20周年院庆系列学术讲座(第31期)暨深圳大学管理科学前沿论坛(第47期)

来源: 发布时间:2017-09-14 16:12:01 浏览量:
    讲座题目: One day ahead wind speed forecasting: A resampling-based approach
    主讲人:赵伟刚 博士(北京理工大学能源与环境政策研究中心在站博士后)                           
    时间: 2017年9月19日(周二)10:00am-12:00pm             
    地点: 文科楼管理学院资料室2423A 
    主持人:秦全德 博士
    内容简介                                                 
    This study develops a nonlinear autoregressive (exogenous) model for one-day-ahead mean hourly wind speed forecasting, where general regression neural network is employed to model nonlinearities of the system. Specifically, this model is a two-stage method consisting of the model selection and training stage along with the iterative forecasting and correcting stage. In the former stage, the model is in the series-parallel configuration, and its test error is estimated by the cross-validation (CV) method. With the help of ARIMA identification results, CV errors are minimized by the Fibonacci search method to select the best lag structure and the only adjustable parameter. In the latter stage, the model is in the parallel configuration, and the so-called leave-one-day-out resampling method is proposed to iteratively estimate correction parameters for horizons up to 24 h ahead, which holds out each full-day data segment from the sample of observations in turn to faithfully reproduce the entire process of training, iterative forecasting and correcting in the in-sample period. Finally, the out-of-sample corrected forecasts can be successively obtained by using the model selected and trained in the former stage and the correction parameters estimated in the latter stage.
 
    主讲人简介 
    赵伟刚,2015年毕业于兰州大学计算数学专业,获理学博士学位,现为北京理工大学能源与环境政策研究中心博士后,主要从事风速风电预测、预测理论与方法、能源政策、机器学习等方面的研究,发表学术论文10余篇,其中SCI/SSCI检索9篇,2篇论文(Renewable Energy, 37 (2012) 241-249、Omega, 45 (2014) 80-91)被列入ESI 高被引论文(Highly Cited Papers)和研究前沿(Research Fronts)。目前为Omega、Applied Energy、Energy、IEEE Transactions on Systems, Man and Cybernetics: Systems等20种SCI检索期刊的审稿人。
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深圳大学 管理学院
管理科学系
2017年9月14日
 
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