Accurate measurements of global solar radiation and atmospheric temperature, as well
as the availability of the predictions of their evolution over time, are important
for different areas of applications, such as agriculture, renewable energy and energy
management, or thermal comfort in buildings. For this reason, an intelligent, light-weight
and portable sensor was developed, using artificial neural network models as the time-series
predictor mechanisms. These have been identified with the aid of a procedure based
on the multi-objective genetic algorithm. As cloudiness is the most significant factor
affecting the solar radiation reaching a particular location on the Earth surface,
it has great impact on the performance of predictive solar radiation models for that
location. This work also represents one step towards the improvement of such models
by using ground-to-sky hemispherical colour digital images as a means to estimate
cloudiness by the fraction of visible sky corresponding to clouds and to clear sky.
The implementation of predictive models in the prototype has been validated and the
system is able to function reliably, providing measurements and four-hour forecasts
of cloudiness, solar radiation and air temperature.