Abstract:

With the continuous construction of new power systems, it is extremiy urgent to study the correlation characteristics between new energy power and meteorology.A new energy power rolling prediction algorithm based on sensitive meteorological factor feature screening and PSO-SVM model optimization is proposed.Firstly,based on the Pearson correlation coefficient and mutual information entropy,the correlation characteristics between meteorological factors and new energy power are analyzed.Based on the D-S evidence theory,the optimized combination of correlation indicators is calculated to screen sensitive meteorological feature factors.The particle swarm optimization (PSO) algorithm is used to globally optimize the parameters of the support vector machine (SVM) new energy power generation prediction model.Then,combined with massive new energy operation data, a rolling prediction model is established.Finally,through experimental verification and analysis,the results show that the proposed prediction model can effectively improve the accuracy of new energy generation prediction.

Key words: new energy, sensitive meteorological feature factor, feature screening, PSO-SVM model, rolling prediction