Distributed Photovoltaic Output Impact Correlation Analysis and Prediction Accuracy Improvement Methods

Improving the prediction accuracy is the core of photovoltaic power prediction. This paper analyzes the key influencing factors of prediction accuracy. Firstly, the Pearson correlation coefficient method is used to analyze the correlation of meteorological influencing factors, and the main meteorological influencing elements of photovoltaic power are determined. Additionally, for the limitation of a single prediction method, a combined prediction modeling algorithm based on particle swarms (PSO) is proposed, which improves the accuracy of prediction from the aspect of modeling algorithms. Finally, based on the method proposed in this paper and combined with the actual operation data of distributed photovoltaic in a region of Hebei Province, an experimental validation system is set up to carry out example analysis. The results show that the proposed method can significantly improve the accuracy of distributed photovoltaic power prediction, which is of great practical application value.