Modelling Residential Supply Tasks Based on Digital Orthophotography Using Machine Learning
This provides a more granular approach for researchers and planners to assess grid challenges from electric vehicle charging, though it is incremental as it builds on existing supply task methodologies.
The paper tackles the problem of modeling residential electricity demand for grid integration of electric vehicles by developing a method that uses digital orthophotography to identify buildings, classify types, and determine demand, with results showing an average 9% deviation from a reference method.
In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previous research work utilizes data that is not always complete or sufficiently granular in space. This is why this paper presents a methodology which allows a holistic determination of residential supply tasks based on orthophotos. To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined. In an exemplary case study, we validate the presented methodology and compare the results with another supply task methodology. The results show that the electricity demand deviates from the results of a reference method by an average 9%. Deviations result mainly from the parameterization of the selected residential building types. Thus, the presented methodology is able to model supply tasks similarly as other methods but more granular.