Estimation of global horizontal irradiance in China using a deep learning method

Quasi-real-time estimation of Global Horizontal Irradiance (GHI) is a key parameter for many solar energy applications. We propose the use of a deep belief network (DBN) to estimate GHI under all-sky conditions derived from Himawari-8 satellite images with a high accuracy and high efficiency, and a high spatial and time resolution for a large geographical area. The DBN solver for GHI (DBN-GHI) is based upon a radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to maintain the balance between computational efficiency and accuracy. The computational time of DBN-GHI for one satellite image with more than 400,000 pixels is around 9 seconds. Aerosol was considered as the main attenuation factor for clear skies, while cloud parameters were used for cloudy-sky GHI estimation. The main novelty of this research is that prior to it, there is a dearth of GHI estimations in China at minutely or hourly intervals in all sky conditions. The results of hourly comparison of this with ground-based observations gave a very good Pearson correlation coefficient (r), above 0.95, with a Root-Mean-Square-Error (RMSE) between about 30 to 80 w m(-2).

Automated summary

The study proposes using a deep belief network to estimate Global Horizontal Irradiance with high accuracy and efficiency. This method fills the gap of GHI estimations in China at minutely or hourly intervals in all sky conditions.