Short-Term Photovoltaic Power Prediction Using a Hybrid Model Based on RIME and IAOA
Photovoltaic power generation is becoming a focal point in the development of new energy sources today.Especially,the prediction of photovoltaic power is becoming a major research direction.To improve the accuracy and efficiency of photovoltaic power prediction,the model of RIME-VMD-IAOA-LSTM is proposed.The model enhances the decomposition efficiency by optimizing the parameters of variational mode decomposition(VMD)while the frost ice optimization algorithm(IAOA)is adopted.A dynamic boundary strategy with a cosine control factor is utilized to regulate the growth rate of the AOA values,thereby,the precision and stability of the algorithm is improved.An adaptive T-distribution mutation strategy is adopted to enhance the local search capability and global exploration ability of AOA in order to effectively avoid the local optima.The integration of two intelligent optimization algorithms significantly improves the prediction efficiency and the speed of the overall model.The results demonstrate that the combined model RIME-VMD-IAOA-LSTM achieves higher accuracy of photovoltaic power prediction compared with that of other prediction models.
frost ice optimization algorithm(RIME)variational mode decomposition(VMD)arithmetic optimization algorithm(AOA)cosine control factor strategyadaptive T-distribution strategyshort-term photovoltaic power prediction
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