深圳大学学报（理工版）2024，Vol.41Issue(3)：293-302,10.DOI:10.3724/SP.J.1249.2024.03293
基于残差的分布式光伏发电功率组合预测方法
Skip-based combined prediction method for distributed photovoltaic power generation
摘要
Abstract
Distributed photovoltaics(PV)power generation forecasting plays an important role in ensuring the safety of power grid operation and nearby consumption.In order to enhance the accuracy of distributed PV power generation forecasting,we propose a meteorological feature extraction method and futher design a PV power generation forecasting model based on skip-connect models fusion.In the feature extraction,we use statistical analysis,features cross-correlation,periodicity information,approximate entropy,and the temperature of PV panels to achieve deep feature extraction of time,weather,and power generation data,enriching the model inputs.In model construction,we propose a multi-layer model fusion method based on residual connections.Firstly,we introduce a k-nearest neighbor(kNN)-based softmax regression prediction model.Secondly,we design a three-layer model structure with multiple prediction models fused through residual connections and multi-layer stacking,continuously impg the prediction accuracy of PV power generation forecasting.Based on the real data of electric power companies,we compare the proposed method with others,such as random forest(RF),TabNet and extreme gradient boosting(XGBoost)for photovoltaic power generation prediction.The results show that the proposed model can reduce the root mean square error,mean absolute error,mean squared error,and mean absolute percentage error by 0.109 7,0.059 1,0.050 7,and 0.036 8 respectively,and improve the goodness of fit by 0.080 4.The feature extraction method based on multi-meteorological factors and the photovoltaic power generation prediction model based on residual connections for multi-model fusion effectively improve the accuracy and stability of distributed PV power generation forecasting.关键词
人工智能/太阳能/特征提取/残差连接/随机森林/TabNet/极端梯度提升/功率预测Key words
artificial intelegient/solar energy/feature extraction/skip-connect/random forest/TabNet/extreme gradient boosting/power prediction分类
信息技术与安全科学引用本文复制引用
吴明朗,庞振江,洪海敏,占兆武,靳飞,唐远洋,叶璇..基于残差的分布式光伏发电功率组合预测方法[J].深圳大学学报（理工版）,2024,41(3):293-302,10.基金项目
Natural Science Foundation of Guangdong Province(2023A1515011667) （2023A1515011667）
Shenzhen Basic Research Foundation(JCYJ20220818100205012,JCYJ20210324093609026) 广东省自然科学基金资助项目(2023A1515011667) （JCYJ20220818100205012,JCYJ20210324093609026）
深圳市基础研究资助项目(JCYJ20220818100205012,JCYJ20210324093609026) （JCYJ20220818100205012,JCYJ20210324093609026）
