宁夏电力Issue(3)：8-13,20,7.DOI:10.3969/j.issn.1672-3643.2024.03.002
基于CNN-XGBoost模型的光伏功率预测
Photovoltaic power prediction based on a combined CNN-XGBoost model
摘要
Abstract
To enhance the accuracy of photovoltaic power forecasts,this study begins with data preprocessing,which involves missing values and normalization.Pearson correlation coefficient is then used to analyze meteorological factors that best correlate with photovoltaic power,thereby reducing the model's input dimensions and complexity.Finally,a combined predictive model is tested using a convolutional neural network and extreme gradient boosting(CNN-XGBoost)model.The test results show that the proposed model successfully enhances the accuracy of photovoltaic power forecasts by significantly reducing the root-mean-square error in predictions.关键词
光伏功率预测/卷积神经网络/梯度提升决策树/组合预测模型Key words
photovoltaic power prediction/convolutional neural network/gradient boosting decision tree/combined predictive model分类
信息技术与安全科学引用本文复制引用
李佳怡,张生艳,贺洁..基于CNN-XGBoost模型的光伏功率预测[J].宁夏电力,2024,(3):8-13,20,7.基金项目
国网宁夏电力有限公司科技项目(5229JY22000M) （5229JY22000M）
