PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON GWO-GRU

被引：0

作者：

Chen, Qingming ^{[1
]}

Liao, Hongfei ^{[1
]}

Sun, Yingkai ^{[2
]}

Zen, Yasen ^{[1
]}

机构：

[1] School of Photoelectric Information, Zhongshan Torch Polytechnic, Zhongshan

[2] Vanward Research Institute, Guangdong Vanward New Electric Co.，Ltd, Foshan

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 07期

关键词：

gated recurrent unit; grey wolf optimizer; long short-term memory; photovoltaic power generation; power forecasting; time series;

D O I：

10.19912/j.0254-0096.tynxb.2023-1248

中图分类号：

学科分类号：

摘要：

The long short-term memory network（LSTM）model has the problem of long time consumption or low accuracy when applied to the prediction of photovoltaic power generation. A photovoltaic power power prediction model based on the grey wolf algorithm（GWO）optimized gated recurrent unit（GRU）was proposed. The photovoltaic power prediction model is established by the approximate optimal hyperparameter，which is obtained by the GWO algorithm. The results show that in terms of long-term power prediction，the GWO-GRU model has lower root mean square error，higher fitting coefficients，and less time consumption，with an average absolute error reduction of 10.20% compared to traditional LSTM models. In terms of short-term power prediction，the GWO-GRU model not only has the lowest average prediction error and the strongest stability under three typical weather conditions，but also saves 17.24% of the average time compared to the GWO-LSTM model. Power predictions of different durations indicate that GWO-GRU performs better in predicting photovoltaic power compared to LSTM. © 2024 Science Press. All rights reserved.

引用

页码：438 / 444

页数：6

共 17 条

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