  The data acquisition of solar radiation in a locality is essential for the
development of efficient designs of systems, whose operation is based on solar
energy. This paper presents a methodology to estimate solar irradiance using an
empiric-stochastic approach, which consists of the computation of normalization
parameters from solar irradiance data. For this study, solar irradiance data
was collected with a weather station during a year. Post-treatment included a
trimmed moving average, to smooth the data, the performance a fitting procedure
using a simple model, to recover normalization parameters, and the estimation
of a probability density map by means of a kernel density estimation method.
The normalization parameters and the probability density map allowed us to
build an empiric-stochastic methodology that generates an estimate of the solar
irradiance. In order to validate our method, simulated solar irradiance has
been used to compute the theoretical generation of solar power, which in turn
has been compared to experimental data, retrieved from a commercial
photovoltaic system. Since the simulation results show a good agreement has
been with the experimental data, this simple methodology can estimate the solar
power production and may help consumers to design and test a photovoltaic
system before installation.
