标题:A Prediction Approach of Hot Electric Power Researches for Heterogeneous Time Spans
作者:Ma, Yan ;Qi, Wei ;Qi, Dali ;Chen, Yufeng ;Zou, Lida
通讯作者:Zou, Lida
作者机构:[Ma, Yan ;Qi, Dali ;Chen, Yufeng ] State Grid Shandong Electric Power Research Institute, Jinan; 250002, China;[Zou, Lida ] Shandong University of Fin 更多
会议名称:2019 6th International Conference on Advanced Composite Materials and Manufacturing Engineering, ACMME 2019
会议日期:22 June 2019 through 23 June 2019
来源:IOP Conference Series: Materials Science and Engineering
出版年:2019
卷:612
期:4
DOI:10.1088/1757-899X/612/4/042068
摘要:Recently with the rapid growth of electric power literatures, it is hard to artificially track and process hot electric scientific researches. In the past most, professionals use simple statistics to get high-frequency words, which is time-consuming and ignores the similarity between words. Moreover, different researchers have different requirements for prediction time span. In the paper we propose a prediction system for hot electric scientific researches and gives its implementation. It is based on our previous work and we improve it to suit indefinite prediction period. The proposed embedded RNN prediction model is flexible for heterogeneous time spans and can return prediction results rapidly and accurately. Our extensive experiments demonstrated that our approach has acceptable precision ratio as well as training time in comparison to SVM, RNN and linear regression algorithms. It also performs better when the embedded layers are multiple. © Published under licence by IOP Publishing Ltd.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074455073&doi=10.1088%2f1757-899X%2f612%2f4%2f042068&partnerID=40&md5=9aa46f82cca60c15cf27fca70c3927f0
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