Modelling Residential Supply Tasks Based on Digital Orthophotography Using Machine Learning

TL;DR

This paper introduces a machine learning-based methodology to model residential electricity demand from orthophotos, enabling more granular and accurate supply task estimation for electrical grid planning.

Contribution

It presents a novel approach that combines building identification, classification, and demand estimation from orthophotos, improving granularity over previous methods.

Findings

Electricity demand deviates by an average of 9% from reference methods.

Methodology provides more granular supply task modeling.

Validation shows comparable accuracy to existing approaches.

Abstract

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.