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

Abstract

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.