Spatio-temporal prediction for distributed PV generation system based on deep learning neural network model

被引:5
作者
Dai, Qiangsheng [1 ]
Huo, Xuesong [1 ]
Hao, Yuchen [1 ]
Yu, Ruiji [2 ]
机构
[1] State Grid Jiangsu Elect Power Co Ltd, Nanjing, Peoples R China
[2] State Grid Xuzhou Power Supply Co Jiangsu Elect Po, Nanjing, Peoples R China
关键词
CNN-LSTM; spatio-temporal; deep learning; distributed PV generation system; PV prediction; CNN-LSTM; POWER;
D O I
10.3389/fenrg.2023.1204032
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
To obtain higher accuracy of PV prediction to enhance PV power generation technology. This paper proposes a spatio-temporal prediction method based on a deep learning neural network model. Firstly, spatio-temporal correlation analysis is performed for 17 PV sites. Secondly, we compare CNN-LSTM with a single CNN or LSTM model trained on the same dataset. From the evaluation indexes such as loss map, regression map, RMSE, and MAE, the CNN-LSTM model that considers the strong correlation of spatio-temporal correlation among the 17 sites has better performance. The results show that our method has higher prediction accuracy.
引用
收藏
页数:11
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