Photovoltaic Power Prediction Model Based on TDE-SO-AWM-GRU

doi:10.11930/j.issn.1004-9649.202401015

Abstract

Abstract:

Accurate prediction of photovoltaic (PV) power is of great significance for the safe and economic operation of power grids, but PV power generation is characterized by time-varying, intermittent, fluctuating, and high nonlinearity characteristics due to the combined effects of multiple meteorological factors, which makes it difficult to deeply explore the implicit information of the data. To solve such problems, a PV power prediction model based on time-varying data enhancement (TDE), snake optimizer (SO), adaptive weight module (AWM), and gated recurrent unit (GRU) was proposed. The expression of data features was improved by TDE with strong correlation, and a new input matrix was constructed. Then, the enhanced input matrix was automatically weighted by AWM and entered into GRU for prediction. At the same time, by considering the difficulty of hyperparameter selection of the combined model, SO was introduced to find the optimal threshold of the model to maximize the performance of the model. Finally, the model was validated using actual data from a PV power station, and the results show that the proposed model can effectively improve the prediction accuracy of PV power.

Key words: photovoltaic power prediction, data enhancement, adaptive weight module, snake optimizer

Hanzhang LI, Jiangtao FENG, Pengcheng WANG, Haojie RONG, Yuhuan CHAI. Photovoltaic Power Prediction Model Based on TDE-SO-AWM-GRU[J]. Electric Power, 2024, 57(12): 41-49.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401015

https://www.electricpower.com.cn/EN/Y2024/V57/I12/41

Figures/Tables 15

Fig.1 Processing flowchart of initial input matrix

Table 1 Classification criteria for change of amplitude of each meteorological factor

幅度	辐照度幅值变化/ (W·m^–2·(15 min)^–1)	湿度幅值变化/ (%·(15 min)^–1)	温度幅值变化/ (℃·(15 min)^–1)
低幅	[0, 80)	[0, 0.8)	[0, 0.3)
中幅	[80, 250)	[0.8, 2.5)	[0.3, 0.8)
高幅	[250, 500)	[2.5, 4)	[0.8, 1.4)
剧幅	≥500	≥4	≥1.4

Fig.2 Amplitude of each meteorological factor varying with season

Fig.3 AWM

Fig.4 GRU

Fig.5 Overall structure of AWM-GRU network

Fig.6 Establishment of SO-AWM-GRU prediction model

Fig.7 Processing process of actual data

Table 2 Comparison of results from stable weather experiments

模型类型	E_MA/MW	E_RMS/MW
LSTM	2.1256	2.4432
GRU	2.0154	2.3839
TDE-LSTM	1.9362	2.3153
TDE-GRU	1.7780	2.0830
CNN-GRU	1.5859	1.7874
Attention-GRU	1.3632	1.5981
TDE-SO-AWM-LSTM	1.4763	1.6992
TDE-SO-AWM-GRU	1.0490	1.1828

Table 3 Comparison of results from mutant weather experiments

模型类型	E_MA/MW	E_RMS/MW
LSTM	2.8256	3.4832
GRU	2.6994	3.3379
TDE-LSTM	2.3950	3.1100
TDE-GRU	2.2350	2.8890
CNN-GRU	2.1426	2.7364
Attention-GRU	1.9785	2.4658
TDE-SO-AWM-LSTM	2.0060	2.5964
TDE-SO-AWM-GRU	1.7563	2.3147

Fig.8 Comparison of LSTM and TDE-LSTM prediction error bands

Fig.9 Comparison of GRU and TDE-GRU prediction error bands

Fig.10 Fitness curve of SO

Fig.11 Comparison of results from stable weather prediction

Fig.12 Comparison of results from mutant weather prediction

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