LAUSR

Most studies about the solar forecasting topic do not analyze and exploit the temporal and spatial components that are inherent to such a task. Furthermore, they mostly focus just on precision and not on other meaningful features, such as flexibility and robustness. With the current energy production trends, where many solar panels are distributed across city rooftops, there is a need to manage all this information simultaneously and to be able to add and remove sensors as needed. Likewise, robust models need to be able to cope with (inevitable) sensor failure and continue producing reliable predictions. Due to all of this, solar forecasting models need to be as decoupled as possible from the number of data sources that feed them and their geographical distribution, enabling also the reusability of the models. This article contributes with a family of Deep Learning models for solar irradiance forecasting complying with the aforementioned features, i.e. flexibility and robustness. In the first stage, several Artificial Neural Networks are trained as a basis for predicting solar irradiance on several locations at the same time. Thereupon, a family of models that work with irradiance maps thanks to Convolutional Long Short-Term Memory layers is presented, obtaining forecast skills between 7.4% and 41% (depending on the location and horizon) compared to the baseline. The latter family comes with flexibility and robustness features, which are required in large-scale Intelligent Environments, such as Smart Cities. Working with irradiance maps means that new sensors can be added (or removed) as needed, without requiring rebuilding the model. Experiments carried out show that sensor failures have a mild impact on the prediction error for several forecast horizons.