电力系统及其自动化学报2024，Vol.36Issue(8)：122-132,11.DOI:10.19635/j.cnki.csu-epsa.001474
考虑特征选择的短期光伏功率组合预测模型
Combined Prediction Model for Short-term Photovoltaic Power Considering Feature Selection
摘要
Abstract
Aimed at the problems in photovoltaic power prediction such as too many characteristic factors,difficulty ineffectively mining the mapping relationship between key features and power,and low prediction accuracy,a combined prediction model is proposed,which integrates the feature selection using the random forest(RF)algorithm and the Gaussian process regression(GPR)model optimized by the grey wolf optimizer(GWO)algorithm. First,Pearson and Spearman correlation coefficients are used to conduct a correlation analysis of features and further perform preliminary screening. Second,based on the RF algorithm,feature importance evaluation is conducted to select the optimal subset of features. Third,the GWO algorithm is employed to optimize the GPR model. Finally,the optimal feature subset is in-putted into the combined prediction model of RF-GWO-GPR for short-term photovoltaic power prediction. The results of simulation experiments based on the measurement data from one photovoltaic power station show that the proposed mod-el can effectively select features under different weather conditions. Compared with that of the single model without fea-ture selection,the prediction accuracy of the new model is significantly improved,and it is significantly better than the combined prediction model composed of other optimization algorithms and GPR model.关键词
光伏功率预测/特征选择/随机森林算法/灰狼优化算法/高斯过程回归Key words
photovoltaic power prediction/feature selection/random forest(RF)algorithm/grey wolf optimizer(GWO)algorithm/Gaussian process regression(GPR)分类
信息技术与安全科学引用本文复制引用
张赟宁,魏广军..考虑特征选择的短期光伏功率组合预测模型[J].电力系统及其自动化学报,2024,36(8):122-132,11.基金项目
国家自然科学基金资助项目(62233006) （62233006）
