  Due to the increasing integration of solar power into the electrical grid,
forecasting short-term solar irradiance has become key for many applications,
e.g.~operational planning, power purchases, reserve activation, etc. In this
context, as solar generators are geographically dispersed and ground
measurements are not always easy to obtain, it is very important to have
general models that can predict solar irradiance without the need of local
data. In this paper, a model that can perform short-term forecasting of solar
irradiance in any general location without the need of ground measurements is
proposed. To do so, the model considers satellite-based measurements and
weather-based forecasts, and employs a deep neural network structure that is
able to generalize across locations; particularly, the network is trained only
using a small subset of sites where ground data is available, and the model is
able to generalize to a much larger number of locations where ground data does
not exist. As a case study, 25 locations in The Netherlands are considered and
the proposed model is compared against four local models that are individually
trained for each location using ground measurements. Despite the general nature
of the model, it is shown show that the proposed model is equal or better than
the local models: when comparing the average performance across all the
locations and prediction horizons, the proposed model obtains a 31.31% rRMSE
(relative root mean square error) while the best local model achieves a 32.01%
rRMSE.
