Total Solar Irradiance Forecasting with Keras Recurrent Neural Networks

Abstract

The prediction of solar irradiance at the top of the atmosphere is useful for research that analyzes the behavior and response of the different layers of the Earth’s atmosphere to variations in solar activity. It would also be useful for the reconstruction of the measurement history (time series) of different instruments that suffered from time failures and discrepancies in scales due to the calibration of equipment. In this work we compare three Keras recurrent neural network architectures to perform forecast of the total solar irradiance. The experiments are part of a larger proposal for modularization of the prediction workflow, which uses digital images of the Sun as input, and aims to make the process modular, accessible and reproducible.