QIENet: Quantitative irradiance estimation network using recurrent
  neural network based on satellite remote sensing data
Longfeng Nie Yuntian Chen Dongxiao Zhang Xinyue Liu Wentian Yuan
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 R2 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{\deg} * 0.02{\deg}(approximately 2km * 2km) for many applied
energy fields.