Hybrid teaching–learning artificial neural network for city-level electrical load prediction  

作　　者：Kangji LI Xianming XIE Wenping XUE Xu CHEN 

机构地区：[1]School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China

出　　处：《Science China(Information Sciences)》2020年第5期208-210,共3页中国科学（信息科学）（英文版）

基　　金：National Natural Science Foundation of China(Grant Nos.61873114,51705206);China Postdoctoral Science Foundation(Grant Nos.2018T110457,2016M601741);Project Foundation for Priority Academic Program Development of Jiangsu Higher Education Institutions。

摘　　要：Dear editor,Rapid economic growth,accompanied by structural changes,strongly affects the global trend of electrical energy consumption.Short-term electrical load forecasting plays a significant role in efficient energy management,fault diagnosis,and power system optimization.

关 键 词：TEACHING ELECTRICAL EDITOR 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM715[自动化与计算机技术—控制科学与工程]