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In order to achieve the climate targets, electrification of individualmobility is essential. However, grid integration of electrical vehicles poseschallenges for the electrical distribution network due to high charging powerand simultaneity. To investigate these challenges in research studies, thenetwork-referenced supply task needs to be modeled. Previous research workutilizes data that is not always complete or sufficiently granular in space.This is why this paper presents a methodology which allows a holisticdetermination of residential supply tasks based on orthophotos. To do this,buildings are first identified from orthophotos, then residential buildingtypes are classified, and finally the electricity demand of each building isdetermined. In an exemplary case study, we validate the presented methodologyand compare the results with another supply task methodology. The results showthat the electricity demand deviates from the results of a reference method byan average 9%. Deviations result mainly from the parameterization of theselected residential building types. Thus, the presented methodology is able tomodel supply tasks similarly as other methods but more granular.

Modelling Residential Supply Tasks Based on Digital Orthophotography Using Machine Learning