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Research on Medium and Long Term Generation Side Deviation Prediction of New Power Market Based on Multi-Layer LSTM

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成果类型:
期刊论文
作者:
Qu Hong;Ye Ze;Weixuan Liang
作者机构:
School of Control and Computer Engineering, North China Electric Power University, Baoding, P.R. China
School of Economics and Management, Changsha University of Science & Technology, Changsha, Hubei, P.R. China
[Qu Hong; Ye Ze] School of Economics and Management, Changsha University of Science & Technology, Changsha, Hubei, P.R. China
[Weixuan Liang] School of Control and Computer Engineering, North China Electric Power University, Baoding, P.R. China
语种:
英文
关键词:
New electricity market;medium and long-term electricity trading;deviation prediction;power generation side;LSTM;multilayer long short memory network.
期刊:
Recent Advances in Electrical & Electronic Engineering
ISSN:
2352-0965
年:
2023
卷:
16
期:
6
页码:
644-653
机构署名:
本校为其他机构
院系归属:
经济与管理学院
摘要:
Background: With the large-scale grid connection operation of new or renewable energy and the access of active loads such as electric vehicles and air conditioners, the electric energy trading business in the power market faces problems such as the rapid expansion of the number of market settlement subjects, explosive growth, various subjects responsible for deviation assessment, various electric energy trading methods and so on.Objective: This paper focuses on the medium and long-term generation side power trading in the new power market. Through cause analysis, induction and summary, algorit...

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