文章基本信息

标题：Incremental Distribution Network Forecasting for Different Industries Based on Long and Short Term Memory Network
本地全文：下载
作者：Yongshang Ji ; Wensheng Li ; Shuhuai An 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2022
卷号：352
页码：1-5
DOI：10.1051/e3sconf/202235203003
语种：English
出版社：EDP Sciences
摘要：In order to improve the quantitative evaluation and prediction ability of power grid investment benefit and solve the optimization problem of investment rhythm caused by the increase of user load in the new park, an incremental distribution network load forecasting method based on long-term and short-term memory network is proposed. Considering multiple influencing factors of investment decision-making, grey correlation analysis is carried out on the factors affecting saturated load, so as to quantitatively determine the impact Degree and size. Then, the influencing factors are used as independent variables, and the demand of electric power or electricity is used as the dependent variables to establish the prediction model to realize the medium and long-term load high-precision forecasting under the condition of small sample and high uncertainty.
关键词：Medium-and long-term load forecasting;deep learnining;industrial classification;incremental distribution network