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.

翻译：全球水平辐照度（GHI）在估算用于产生可持续绿色能源的太阳能资源中起着关键作用。为获取高空间分辨率的GHI估计值，本文提出了一种名为QIENet的定量辐照度估计网络。具体而言，该网络分别通过循环神经网络（RNN）和卷积运算提取并融合向日葵8号卫星遥感数据的时空特征。QIENet的输入不仅包含遥感数据，还整合了与GHI相关的时间信息（小时、日、月）及地理信息（海拔、经度、纬度）。根据年太阳能量空间分布，推荐将卫星光谱通道B07、B11-B15及时间作为模型输入。同时，QIENet能够捕捉不同云层对逐时GHI估计的影响。更重要的是，QIENet不会高估地面观测值，且相比ERA5/NSRDB，其RMSE降低27.51%/18.00%，R²提升20.17%/9.42%，r提升8.69%/3.54%。此外，QIENet可为众多能源应用领域提供空间分辨率为0.02°×0.02°（约2km×2km）的高保真逐时GHI数据库。