Short-term photovoltaic power forecasting based on Attention-GRU model

被引：0

作者：

Liu G. ^{[1
]}

Sun W. ^{[1
]}

Wu Z. ^{[2
]}

Chen Z. ^{[1
]}

Zuo Z. ^{[1
]}

机构：

[1] School of Electrical and Information Engineering, Jiangsu University, Zhenjiang

[2] Jiangsu Zhen An Power Equipment Co., Ltd., Zhenjiang

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 02期

关键词：

Attention mechanism; Gated recurrent unit; Neural network; Photovoltaic power generation; Power forecasting;

D O I：

10.19912/j.0254-0096.tynxb.2020-1202

中图分类号：

学科分类号：

摘要：

In view of the large number of parameters of traditional long-short term memory neural network (LSTM) and lack of important timing information when processing long-term sequences, an Attention-GRU short-term photovoltaic power forecasting model combining attention mechanism and gated recurrent unit (GRU) is proposed. Firstly, a forecasting model for different weather types is established. Then, GRU is used to extract the time series characteristics of photovoltaic power generation and the attention mechanism is introduced to strengthen the attention to important information in the time series input. Finally, a forecasting model for different weather types is established. Simulation results show that the proposed Attention-GRU model has higher forecasting accuracy than the comparison models. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：226 / 232

页数：6

共 18 条

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