基于CEEMD-POA-BiLSTM的短期光伏功率预测OA

With the rapid development of renewable energy,the photovoltaic power generation,as an important component of renewable energy,its power prediction accuracy plays a significant role in the stable operation of power grids and energy management.To accurately predict photovoltaic power and solve power grid dispatching problems,a hybrid deep learning model combining Complementary Ensemble Empirical Mode Decomposition(CEEMD),Pelican Optimization Algorithm(POA),and Bidirectional Long Short-Term Memory(BiLSTM)is proposed.Initially,the Pearson correlation coefficient is used to determine irradiance and module temperature as inputs.Then,the original data is decomposed using CEEMD to extract multi-scale features,and on this basis,the POA algorithm is introduced to optimize the initial learning rate,hidden layer nodes,and regularization coefficient of the BiLSTM model.Finally,the comparative experiments are conducted on different decomposition algorithms.The results show that the proposed CEEMD-POA-BiLSTM model excels in short-term photovoltaic power prediction tasks,achieving higher prediction accuracy compared to other models.