  With increasing concerns of climate change, renewable resources such as
photovoltaic (PV) have gained popularity as a means of energy generation. The
smooth integration of such resources in power system operations is enabled by
accurate forecasting mechanisms that address their inherent intermittency and
variability. This paper proposes a probabilistic framework to predict
short-term PV output taking into account the uncertainty of weather. To this
end, we make use of datasets that comprise of power output and meteorological
data such as irradiance, temperature, zenith, and azimuth. First, we categorise
the data into four groups based on solar output and time by using k-means
clustering. Next, a correlation study is performed to choose the weather
features which affect solar output to a greater extent. Finally, we determine a
function that relates the aforementioned selected features with solar output by
using Gaussian Process Regression and Matern 5/2 as a kernel function. We
validate our method with five solar generation plants in different locations
and compare the results with existing methodologies. More specifically, in
order to test the proposed model, two different methods are used: (i) 5-fold
cross-validation; and (ii) holding out 30 random days as test data. To confirm
the model accuracy, we apply our framework 30 independent times on each of the
four clusters. The average error follows a normal distribution, and with 95%
confidence level, it takes values between -1.6% to 1.4%.
