Schreiber, JensBuschin, ArtjomSick, BernhardDavid, KlausGeihs, KurtLange, MartinStumme, Gerd2019-08-272019-08-272019978-3-88579-688-6https://dl.gi.de/handle/20.500.12116/25026Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons. However, these estimates allow only a limited and highly uncertain use in the planning of electric power distribution. More reliable planning processes require considerably more information about future forecast quality. In this article, we present an in-depth analysis and comparison of influencing factors regarding uncertainty in wind and photovoltaic power forecasts, based on four different machine learning (ML) models. In our analysis, we found substantial differences in uncertainty depending on ML models, data coverage, and seasonal patterns that have to be considered in future planning studies.enuncertainty analysismachine learning modelsseasonal effectsdata coverageText/Conference Paper10.18420/inf2019_741617-5468