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To mitigate the uncertainty of variable renewable resources, twooff-the-shelf machine learning tools are deployed to forecast the solar poweroutput of a solar photovoltaic system. The support vector machines generate theforecasts and the random forest acts as an ensemble learning method to combinethe forecasts. The common ensemble technique in wind and solar powerforecasting is the blending of meteorological data from several sources. Inthis study though, the present and the past solar power forecasts from severalmodels, as well as the associated meteorological data, are incorporated intothe random forest to combine and improve the accuracy of the day-ahead solarpower forecasts. The performance of the combined model is evaluated over theentire year and compared with other combining techniques.

Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting