电器与能效管理技术Issue(3)：38-45,8.DOI:10.16628/j.cnki.2095-8188.2025.03.006
基于敏感气象特征因子筛选与PSO-SVM模型优化的新能源功率预测特性研究
Research on Power Prediction Characteristics of New Energy Based on Sensitive Meteorological Feature Factor Screening and PSO-SVM Model Optimization
巩伟峥1
作者信息
- 1. 国家电网有限公司华东分部,上海 200120
- 折叠
摘要
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.关键词
新能源/敏感气象特征因子/特征筛选/PSO-SVM模型/滚动预测Key words
new energy/sensitive meteorological feature factor/feature screening/PSO-SVM model/rolling prediction分类
动力与电气工程引用本文复制引用
巩伟峥..基于敏感气象特征因子筛选与PSO-SVM模型优化的新能源功率预测特性研究[J].电器与能效管理技术,2025,(3):38-45,8.
