  To mitigate the uncertainty of variable renewable resources, two
off-the-shelf machine learning tools are deployed to forecast the solar power
output of a solar photovoltaic system. The support vector machines generate the
forecasts and the random forest acts as an ensemble learning method to combine
the forecasts. The common ensemble technique in wind and solar power
forecasting is the blending of meteorological data from several sources. In
this study though, the present and the past solar power forecasts from several
models, as well as the associated meteorological data, are incorporated into
the random forest to combine and improve the accuracy of the day-ahead solar
power forecasts. The performance of the combined model is evaluated over the
entire year and compared with other combining techniques.
