Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model’s capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

The current transition in the European energy sector towards climate neutrality requires detailed and reliable energy system modeling. The quality and relevance of the energy system modeling highly depend on the availability and quality of model input datasets. However, detailed and reliable datasets are still missing, especially for the gas infrastructure. In this contribution, we present our approach for developing an opensource model of the gas transport network in Europe. Various freely available data sources were used to collect gas transport data. The datasets from multiple sources were merged, and further, statistical methods were used to generate missing data.As a result, we successfully created a gas transport network model only using open-source data. The SciGRID_gas model contains 206,000 km of pipeline data which is roughly in accordance to former estimations. In addition, datasets of compressor stations, LNG terminals, storages, production sites, gas power plants, border points, and demand time series are provided. Finally, we have discussed data gaps and how they can potentially be closed.

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