An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production

Renewable energies are the alternative that leads to a cleaner generation and a reduction in CO2 emissions. However, their dependency on weather makes them unreliable. Traditional energy operators need a highly accurate estimation of energy to ensure the appropriate control of the network, since energy generation and demand must be balanced. This paper proposes a forecaster to predict solar irradiation, for very short-term, specifically, in the 10 min ahead. This study develops two tools based on artificial neural networks, namely Long-Short Term Memory neural networks and Convolutional Neural Network. The results demonstrate that the Convolutional Neural Network has a higher accuracy. The tool is tested examining the root mean square error, whichwas of 52.58W/m(2) for the testing step. Compared against the benchmark, it has obtained an improvement of 8.16%. Additionally, for the 82% of the tested days it has given a less than 4% error between the predicted and the actual energy generation. Results indicate that the forecaster is accurate enough to be implemented on a photovoltaic generation plan, improving their integration into the electrical grid, not only for providing power but also ancillary services. (C) 2022 The Author(s). Published by Elsevier Inc. on behalf of International Energy Initiative.