Short-Term Wind Power Forecast Based on CNN&LSTM-GRU Model Integrated with CEEMD-SE Algorithm

doi:10.11930/j.issn.1004-9649.202302098

Abstract

Abstract:

In order to further improve the accuracy of short-term wind power forecast, a CNN & LSTM-GRU based short-term wind power prediction model using CEEMD-SE algorithm is proposed. First, the original wind power output series are decomposed into several intrinsic mode function components and one residual component by complementary set empirical mode decomposition, and those components of similar mode are reconstructed by sample entropy algorithm. Next, the parallel network structure of convolutional neural network and long short term memory network is set up, and the local and temporal features of the data are extracted. And then the features are fused and input into the gated cyclic unit network for learning and prediction. Finally, the feasibility of the model is verified through case studies. The results show that the forecast accuracy has been improved effectively. The root mean square error and average absolute error, of the proposed model are reduced by 15.06% and 15.22% respectively, while coefficient of determination is up by 1.91%.

Key words: short-term wind power forecasting, complementary ensemble empirical mode decomposition, sample entropy, long short term memory network, gated recurrent unite

CLC Number:

TM614

Guohua YANG, Xin QI, Rui JIA, Yifeng LIU, Fei MENG, Xin MA, Xiaowen XING. Short-Term Wind Power Forecast Based on CNN&LSTM-GRU Model Integrated with CEEMD-SE Algorithm[J]. Electric Power, 2024, 57(2): 55-61.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202302098

https://www.electricpower.com.cn/EN/Y2024/V57/I2/55

Figures/Tables 8

Fig.1 LSTM network structure

Fig.2 GRU network structure

Fig.3 CNN&LSTM-GRU prediction model structure

Fig.4 Flow chart of wind power forecast

Table 1 SE values of IMF1-IMF10 and Res

分量	SE值	分量	SE值
IMF1	1.22372	IMF7	0.35957
IMF2	1.01075	IMF8	0.12358
IMF3	0.75592	IMF9	0.02324
IMF4	0.67636	IMF10	0.00304
IMF5	0.65487	RES	0.00046
IMF6	0.60424

Fig.5 Individual component after CEEMD-SE decomposition

Table 2 Comparison of prediction results of various models

预测模型	RMSE/MW	MAE/MW	R²
PSO-SVR	2.505	1.947	0.928
LSTM	2.322	1.767	0.938
CNN-LSTM	2.198	1.656	0.944
CNN&LSTM-GRU	1.867	1.404	0.962

Fig.6 Prediction result curve of each model

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