QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Nie，Longfeng1,2; Chen，Yuntian3; Zhang，Dongxiao1,2,3; Liu，Xinyue1,4; Yuan，Wentian5

2024-03-01

10.1016/j.jag.2023.103584

International Journal of Applied Earth Observation and Geoinformation   影响因子和分区

1569-8432

1872-826X

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11–B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02°×0.02° (approximately 2km×2km) for many applied energy fields.

Global horizontal irradiance High-fidelity hourly GHI database QIENet Recurrent neural network Satellite remote sensing

SCI

英语

第一

2-s2.0-85182890421

Scopus

1.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Peng Cheng Laboratory,Shenzhen,518000,China
3.Ningbo Institute of Digital Twin,Eastern Institute of Technology,Ningbo,315200,China
4.Department of Civil and Environmental Engineering,National University of Singapore,Singapore,117576,Singapore
5.Beijing Kingtansin Technology Company Limited,Beijing,100000,China

Nie，Longfeng,Chen，Yuntian,Zhang，Dongxiao,et al. QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data[J]. International Journal of Applied Earth Observation and Geoinformation,2024,127.

Nie，Longfeng,Chen，Yuntian,Zhang，Dongxiao,Liu，Xinyue,&Yuan，Wentian.(2024).QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data.International Journal of Applied Earth Observation and Geoinformation,127.

Nie，Longfeng,et al."QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data".International Journal of Applied Earth Observation and Geoinformation 127(2024).