Improving solar power forecasting through advanced feature engineering

Abstract

The production of solar energy is undoubtedly dependent on weather conditions and the time of day in which it occurs. Therefore, this source of energy being extremely variable and difficult to predict with high accuracy after many studies, the need for a new approach surges in order to increase it. With this project, it is proposed the use of images as input for deep learning structures with the purpose to achieve automatic feature extraction that can be replicated to other data sets. The images are generated from a grid of numerical weather predictions of, particularly, surface downelling shortwave flux and cloud cover at different levels. In this thesis, several neural network models with be analysed according to its input data: numerical, numerical conjugated with images and images. The models with numerical input data will serve as reference for comparison and evaluation of the added value brought by the images to the forecasts, since the reference's predictions used variables obtained by manual feature extraction.