Day Ahead Market Marginal Price Forecasting Based on GCN-LSTM

被引：0

作者：

Han S. ^{[1
]}

Hu F. ^{[1
]}

Chen Z. ^{[1
]}

Zhang L. ^{[2
]}

Bai X. ^{[3
]}

机构：

[1] State Key Laboratory of Electrical Insulation and Power Equipment (Xi'an Jiao Tong University), Xi'an, 710049, Shaanxi Province

[2] States Grid Shaanxi Economic Research Institute, Xi'an, 710064, Shaanxi Province

[3] Shaanxi Electric Power Trading Center Co., Ltd., Xi'an, 710004, Shaanxi Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Day ahead market; Electricity price forecasting; Graph convolution network; Long short term memory; Spatiotemporal forecasting algorithm;

D O I：

10.13334/j.0258-8013.pcsee.202548

中图分类号：

学科分类号：

摘要：

The commonly used traditional time-domain forecasting method based on time series often, fails to make full use of the regional information of the power market and ignores the extraterritorial influence factors of regional electricity price under cross regional transmission conditions. In order to further improve the accuracy of electricity price forecasting, a spatiotemporal forecasting algorithm (graph convolution network- long short term memory, GCN-LSTM) based on graph convolution network - long short term memory network was proposed. Firstly, the algorithm described the regional distribution of electricity market data by building a graph model, and the graph convolution neural network was used to extract the electricity market data around the studied area to import the outside information transmitted to the region. Secondly, the information extracted from the graph convolution neural network at different times constituted a time series and was input into the long short term memory network, so as to forecast the day ahead market marginal price. The operation data of Nord Pool was used for example analysis. Compared with the control algorithm, the algorithm has better prediction accuracy and universal applicability. © 2022 Chin. Soc. for Elec. Eng.

引用

页码：3276 / 3285

页数：9

共 35 条

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