A photovoltaic power ultra short-term prediction method integrating Hungarian clustering and PSO algorithm

Abstract

In response to the problem of low prediction accuracy in ultra short-term prediction of photovoltaic power, this study combines Hungarian clustering analysis and particle swarm optimization variational mode decomposition algorithm to construct a photovoltaic power ultra short-term forecasting model, to analyze data in depth and improve prediction accuracy. The experiment outcomes show that the Hungarian algorithm performs well in integrating single clustering results and effectively improves the problem of atypical classification. In addition, the clustering ensemble model shows significant improvement compared to other models on the Calinski-Harabasz index, and effectively reduces the overlap between clusters on the Davies-Bouldin index, improving the overall quality of clustering. Under different weather conditions, the convergence accuracy and speed of the multiverse optimization support vector machine, multiverse optimization support vector machine, and particle swarm optimization variational mode decomposition algorithms each have their own advantages, but the particle swarm optimization variational mode decomposition algorithm performs better. In addition, the Hungarian clustering model has high stability in predicting errors, with average absolute error and average relative error lower than BP and RBF models. The maximum absolute error and maximum relative error are reduced, indicating the effectiveness and predictive advantage of the proposed Hungarian clustering ensemble model in predicting photovoltaic power.