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The integration of renewable energy sources (RES) into local energy distribution networks becomes increasingly important. Renewable energy highly depends on weather conditions, making it difficult to maintain stability in such networks. To still enable efficient planning and balancing, forecasts of energy supply are essential. However, typical distribution networks contain a variety of heterogeneous RES installations (e.g. wind, solar, water), each providing different characteristics and weather dependencies. Additionally, advanced meters, which allow the communication of finalgranular production curves to the network operator, are not available at all RES sites. Despite these heterogeneities and missing measurements, reliable forecasts over the whole local distribution network have to be provided. This poses high challenges on choosing the right input parameters, statistical models and forecasting granularity (e.g. single RES installations vs. aggregated data). In this paper, we will discuss such problems in energy supply forecasting using a real-world scenario. Subsequently, we introduce our idea of a generalized optimization approach that determines the best forecasting strategy for a given scenario and sketch research challenges we are planning to investigate in future work.

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