Data for Optimizing Heat Supply Systems in Existing Districts

Abstract

The planning and modernization of heat supply systems in existing districts rely heavily on high-quality data about the building stock. Heat demand densities are a minimum requirement for designing heat networks. Moreover, knowledge of the efficiency status of individual buildings is crucial for determining required supply temperatures, potential reductions in heat demand through future retrofits, and the likely connection rate of buildings to a heat network. To address this, we combine data from various sources to obtain this information with high accuracy and minimal investment of time and resources. Our harmonization process integrates 3D data from public surveying offices, OpenStreetMap, the federal census, regional cadastres, and earth observation data – all of which are freely available in many European countries. Machine learning models are employed to fill data gaps and disaggregate information that is only available in aggregated form. This chain of data cleaning, harmonization, and building classification generates the input for heat demand calculations without the need for on-site inspections. In this contribution, we treat this purely remote analysis as a baseline and explore how it can be extended by data from thermographic drive-through assessments. A car-mounted multi-sensor system equipped with a thermography camera captures a series of images, location and time stamps, and potentially additional data. An automated processing pipeline evaluates the recorded data and estimates the insulation quality of all building facades visible from the streets. This approach enables the estimation of the efficiency and retrofit status of each building for entire districts or towns using real on-site data with minimal human labor. By combining these datasets, we can provide the high-quality information required for district energy concepts and heat network planning, ultimately supporting the development of more efficient and sustainable heat supply systems.