Improved whale migration algorithm and its application to short-term photovoltaic power prediction

To achieve accurate prediction of short-term photovoltaic power generation, based on Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), Convolutional Neural Network (CNN), and Kernel Extreme Learning Machine (KELM), a novel photovoltaic power generation power prediction model is proposed. Firstly, VMD is utilized to decompose the original power sequence to overcome the influence of noise and randomness. Subsequently, a CNN-KELM hybrid model is constructed, and the IWMA algorithm is introduced to optimize its hyperparameters, thereby enhancing the predictive accuracy of the model. Finally, individual VMD-CNN-IWMA-KELM models are constructed for each stationary component to conduct training and prediction. The predictive results of all components are then reconstructed and superimposed to obtain the final photovoltaic power generation forecast. Experimental results demonstrate that, compared to existing models, the proposed model achieves superior prediction accuracy and more accurately characterizes the variation trend of photovoltaic power output.