  A number of industrial applications, such as smart grids, power plant
operation, hybrid system management or energy trading, could benefit from
improved short-term solar forecasting, addressing the intermittent energy
production from solar panels. However, current approaches to modelling the
cloud cover dynamics from sky images still lack precision regarding the spatial
configuration of clouds, their temporal dynamics and physical interactions with
solar radiation. Benefiting from a growing number of large datasets, data
driven methods are being developed to address these limitations with promising
results. In this study, we compare four commonly used Deep Learning
architectures trained to forecast solar irradiance from sequences of
hemispherical sky images and exogenous variables. To assess the relative
performance of each model, we used the Forecast Skill metric based on the smart
persistence model, as well as ramp and time distortion metrics. The results
show that encoding spatiotemporal aspects of the sequence of sky images greatly
improved the predictions with 10 min ahead Forecast Skill reaching 20.4% on the
test year. However, based on the experimental data, we conclude that, with a
common setup, Deep Learning models tend to behave just as a 'very smart
persistence model', temporally aligned with the persistence model while
mitigating its most penalising errors. Thus, despite being captured by the sky
cameras, models often miss fundamental events causing large irradiance changes
such as clouds obscuring the sun. We hope that our work will contribute to a
shift of this approach to irradiance forecasting, from reactive to
anticipatory.
