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基于GRU-RF模型的太阳辐照度短时预测
引用本文:周满国,黄艳国,段锦锋.基于GRU-RF模型的太阳辐照度短时预测[J].太阳能学报,2022,43(7):166-173.
作者姓名:周满国  黄艳国  段锦锋
作者单位:江西理工大学电气与自动化学院,赣州 341000
基金项目:国家自然科学基金(72061016);;江西省教育厅科技项目(GJJ160608);
摘    要:针对现有太阳辐照度短期预测方法的建模复杂、准确度低等问题,提出一种基于深度学习的GRU-RF动态权值组合预测方法。大气因素与太阳辐照度数据融合,将运算速度较快且模型复杂度较低的随机森林(RF)模型与带有时序记忆的门控循环单元(GRU)神经网络进行动态权值的加权集成,分别将地表接收到的太阳辐照度、近地层气温、相对湿度、近地层风速和相对气压等变化特征进行预测研究。通过几种模型对比分析,结果表明使用GRU-RF模型预测短时(9 h)太阳辐照度结果较好,运行速度较快,在不同时间间隔(5、10以及15 min)下能够很好地预测太阳辐照度数据。

关 键 词:太阳辐照度  预测  深度学习  门控循环单元网络  随机森林  时间序列  
收稿时间:2020-10-15

SHORT TERM PREDICTION OF SORAL IRRADIANCE BASED ON GRU-RF MODEL
Zhou Manguo,Huang Yanguo,Duan Jinfeng.SHORT TERM PREDICTION OF SORAL IRRADIANCE BASED ON GRU-RF MODEL[J].Acta Energiae Solaris Sinica,2022,43(7):166-173.
Authors:Zhou Manguo  Huang Yanguo  Duan Jinfeng
Affiliation:School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000,China
Abstract:Aiming at the problems of complex modeling and low accuracy of existing short-term solar irradiance prediction methods, a GRU-RF dynamic weight combination prediction method based on deep learning is proposed. The atmospheric factors and solar irradiance data are fused, and the random forest (RF) model with fast operation speed and low model complexity is integrated with the gated recurrent unit (GRU) neural network with time sequence memory for dynamic weight weighting. The variation characteristics of solar irradiance, surface air temperature, relative humidity, surface wind speed and relative pressure received by the surface are predicted respectively. Through the comparison and analysis of several models, the results show that the GRU-RF model can be used to predict the solar irradiance in short time (9 h) with faster running speed, and can be used to predict the solar irradiance data well in different time intervals (5,10 and 15 min).
Keywords:solar irradiance  forecasting  deep learning  gated recurrent unit network  random forest  time series  
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