Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

To fully exploit the spectral information of modern geostationary satellites, a deep learning framework based on convolutional neural network and attention mechanism is proposed for 5-minute ground-level global horizontal irradiance (GHI) and direct normal irradiance (DNI) estimations. Correlation analysis is performed to select the representative satellite bands, which can improve the modeling efficiency without accuracy loss compared with the usage of all spectral bands. The results show that the proposed model produces GHI estimation with a normalized root mean squared error (nRMSE) of 20.57% and a normalized mean bias error (nMBE) of -2.04%. The DNI estimation has a nRMSE of 23.63% and the nMBE is 0.36%. Compared with the National Solar Radiation Database (NSRDB) based on the physical solar model, the proposed method produces a GHI estimation with the nRMSE reduction of 5.15%. As for DNI estimation, the proposed method shows a nRMSE reduction of 13.77%. Meanwhile, the proposed methods generally yield better GHI and DNI estimations under different intervals of clear-sky index than NSRDB. The combination of deep learning and remote sensing shows potentials in better extracting the cloud information via multispectral satellite images, which can better support solar resourcing and forecasting applications especially under cloudy conditions.