DetEEktor: Mask R-CNN based neural network for energy plant identification on aerial photographs

Abstract

Decentralized renewable energies (RE) represent one of the most important elements for future energy systems. For an optimal integration, a comprehensive knowledge of the installed RE systems and their characteristics is required. In Germany, the 'Marktstammdatenregister' (MaStR) gives access to most of this information but has significant deficits in terms of up to date data covering all installed systems, later illustrated using a ground truth data set, and completely neglects some plant types e.g. solar thermal systems. To address this, we developed the novel model 'DetEEktor', with which six different RE plant types can be simultaneously detected and characterized on aerial photographs by means of a Mask R convolutional neural network. As first contribution of this paper, we give a detailed overview of the structure, design and data base of the model. Afterwards, we contribute by demonstrating its capability to attack the lack of knowledge of existing RE systems: Dependent on the plant type, we succeed in detecting 63 to 75 % of the existing systems. Applied on the ground truth use case, we reduce the number of missing photovoltaic systems in the MaStR by 44 % and identify 72 % of the existing solarthermal systems. Applied to a high RE share use case, we identified an estimated number of about 1,468 photovoltaic (including 18 free-field), 1,063 solarthermal, 6 biomass and 4 wind power plants missing in the MaStR. In conclusion, with our presented 'DetEEktor', we aim to contribute to a more detailed data basis of existing RE systems.