Identification and Handling of Critical Irradiance Forecast Errors Using a Random Forest Scheme – A Case Study for Southern Brazil

Abstract Large forecast errors of solar power prediction cause challenges for the management of electric grids. Here, the classification technique Random Forests is applied to analyze the possible linkage of hourly or daily forecast errors to the actual situation given by a set of meteorological variables. This form a prediction of the forecast error and is thus usable to update the forecast. The performance of this scheme is assessed for the example of irradiance forecasts in Brazil. While limited to none improvements are obtained for next-hour forecasts, significant improvements are obtained for the next-day forecasts.