  Generation and load balance is required in the economic scheduling of
generating units in the smart grid. Variable energy generations, particularly
from wind and solar energy resources, are witnessing a rapid boost, and, it is
anticipated that with a certain level of their penetration, they can become
noteworthy sources of uncertainty. As in the case of load demand, energy
forecasting can also be used to mitigate some of the challenges that arise from
the uncertainty in the resource. While wind energy forecasting research is
considered mature, solar energy forecasting is witnessing a steadily growing
attention from the research community. This paper presents a support vector
regression model to produce solar power forecasts on a rolling basis for 24
hours ahead over an entire year, to mimic the practical business of energy
forecasting. Twelve weather variables are considered from a high-quality
benchmark dataset and new variables are extracted. The added value of the heat
index and wind speed as additional variables to the model is studied across
different seasons. The support vector regression model performance is compared
with artificial neural networks and multiple linear regression models for
energy forecasting.
