An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

• Hourly DNI estimates are generated using an evolutionary ANN ensemble model.
• Inputs are selected from 20 candidates using a genetic algorithm.
• Model exploits all information within infrared Meteosat channels.
• Three years of data measured at 28 ground stations over an extensive area are used.
• The proposed approach has better performance than existing models on hourly basis.

A new evolutionary design of an ANN (artificial neural network) ensemble model is developed to generate hourly DNI (direct normal irradiance) estimates. The procedure combines a genetic algorithm for selecting the best inputs with an ANN ensemble method. The ensemble model was calibrated and evaluated using three years of Meteosat-9 images and data measured at 28 high-quality ground stations over an extensive area, mainly in Europe. The most valuable inputs for DNI estimation are shown to be the following: all Meteosat-9 channels except ch08 and ch11; relative air mass m, integral Rayleigh optical thickness δr, extraterrestrial global irradiance G0, beam clear-sky index Bcs, and the cosine of zenith angle θ. No additional atmospheric information such as turbidity, aerosol optical depth or water vapor content are required for the model. Ensemble estimates were nearly unbiased (MBE = 1.98%) and overall RMSE (root mean square error) was 24.29% across an independent spatial and temporal dataset. This represents an improvement of 35% over other common methods for estimating DNI. The estimates were reasonably reliable in all seasons, and were more accurate in clear-sky conditions.