Photovoltaic (PV) power generation is highly stochastic and volatile, a trait that presents a notable challenge to the prediction accuracy of distributed PV systems. To address this challenge, this study proposes a short-term photovoltaic power prediction strategy that integrates variational modal decomposition (VMD) for feature extraction with an improved RIME (IRIME) optimization algorithm for parameter optimization. Firstly, the raw PV power data are split into several intrinsic mode functions (IMFs) using VMD. The decomposed IMFs are reconstructed by using the sample entropy (SE) method, and a new subsequence with enhanced features is obtained. Secondly, a bidirectional gated recurrent unit (BIGRU) prediction model is established, and its structural parameters are optimized by the IRIME algorithm. Finally, the prediction results of each subsequence are summarized to obtain the final prediction value. Information from a centralized PV power station located in southern China is employed to verify the suggested prediction model. Experimental findings indicate that in comparison with other models, the proposed model achieves the smallest PV power prediction error; the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the proposed model are reduced at least by 21.95%, 3.03%, and 12.33%, respectively. The coefficient of determination (R2) is increased at least by 10.56‰. The method presented in this research is capable of improving prediction accuracy efficiently and holds specific engineering practicality.
Xie et al. (Thu,) studied this question.
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