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In the present paper, we introduce a new method for the automated generationof residential distribution grid models based on novel building load estimationmethods and a two-stage optimization for the generation of the 20 kV and 400 Vgrid topologies. Using the introduced load estimation methods, various open orproprietary data sources can be utilized to estimate the load of residentialbuildings. These data sources include available building footprints fromOpenStreetMap, 3D building data from OSM Buildings, and the number ofelectricity meters per address provided by the respective distribution systemoperator (DSO).  For the evaluation of the introduced methods, we compare the resulting gridmodels by utilizing different available data sources for a specific suburbanresidential area and the real grid topology provided by the DSO. Thisevaluation yields two key findings: First, the automated 20 kV networkgeneration methodology works well when compared to the real network. Second,the utilization of public 3D building data for load estimation significantlyincreases the resulting model accuracy compared to 2D data and enables resultssimilar to models based on DSO-supplied meter data. This substantially reducesthe dependence on such normally proprietary data.

Open Data-Driven Automation of Residential Distribution Grid Modeling with Minimal Data Requirements