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RESEARCH PRODUCT

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

Hans Georg BeyerHans Helmut ZürnManfred Georg KratzenbergPål Preede Revheim

subject

Meteorologybusiness.industryComputer sciencepost-processingIrradianceLinkage (mechanical)Forecast verificationRandom forestlaw.inventionSet (abstract data type)Identification (information)Energy(all)lawsolar irradiance forecastsbusinessConsensus forecastRandom Forest classificationSolar power

description

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.

yearjournalcountryeditionlanguage
2015-08-01Energy Procedia
10.1016/j.egypro.2015.07.900http://dx.doi.org/10.1016/j.egypro.2015.07.900