Short term prediction of soral irradiance based on GRU-RF model

被引：0

作者：

Zhou M. ^{[1
]}

Huang Y. ^{[1
]}

Duan J. ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 07期

关键词：

Deep learning; Forecasting; Gated recurrent unit network; Random forest; Solar irradiance; Time series;

D O I：

10.19912/j.0254-0096.tynxb.2020-1091

中图分类号：

学科分类号：

摘要：

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). © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：166 / 173

页数：7

共 23 条

[1]

SUN H W, ZHAO N, ZENG X F, Et al., Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables, Energy conversion and management, 105, pp. 880-890, (2015)

[2]

TAHER M, AHLEM H, SOUHEIL E A, Et al., A novel solar concentrating system based on a fixed cylindrical reflector and tracking receiver, Renewable energy, 117, pp. 85-107, (2018)

[3]

STJERN C W, KRISTJANSSON J E, HANSEN A W., Global dimming and global brightening-An analysis of surface radiation and cloud cover data in northern Europe, International journal of climatology, 29, 5, pp. 643-653, (2009)

[4]

YANG X, JIANG F, LIU H., Short-term power prediction of photovoltaic plant based on SVM with similar data and wavelet analysis, Przegląd elektrotechniczny, 89, 5, pp. 81-85, (2013)

[5]

CHAABENE M, AMMAR M B., Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems, Renewable energy, 33, 7, pp. 1435-1443, (2008)

[6]

HAMMER A, HEINEMANN D, LORENZ E, Et al., Shortterm forecasting of solar radiation

[7]

a statistical approach using satellite data, Solar energy, 67, 1, pp. 139-150, (1999)

[8]

MELLIT A, PAVAN A M., A 24-h forecast of solar irradiance using artificial neural network

[9]

Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Solar energy, 84, 5, pp. 807-821, (2010)

[10]

MARQUEZ R, COIMBRA C F M., Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database, Solar energy, 85, 5, pp. 746-756, (2011)

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