Concepts and tools to improve the thermal energy performance of buildings and urban districts - diagnosis, assessment, improvement strategies and cost-benefit analyses

Retrofitting existing buildings to optimize their thermal energy performance is a key factor in achieving climate neutrality by 2045 in Germany. Analyzing buildings in their current condition is the first step toward preparing effective and efficient energy retrofit measures. A high-quality building analysis helps to evaluate whether a building or its components are suitable for retrofitting or replacement. Subsequently, appropriate combinations of retrofit measures that create financial and environmental synergies can be determined.
This dissertation is a cumulative work based on nine papers on the thermal analysis of existing buildings. The focus of this work and related papers is on thermography with drones for building audits, intelligent processing of thermographic images to detect and assess thermal weaknesses, and building modeling approaches to evaluate thermal retrofit options. While individual buildings are usually the focus of retrofit planning, this dissertation also examines the role of buildings in the urban context, particularly on a district level. Multiple adjacent buildings offer numerous possibilities for further improving retrofits, such as the economies of scale for planning services and material procurement, neighborhood dynamics, and exchange of experiences between familiar building owners.
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This work reveals the opportunities and obstacles for panorama drone thermography for building audits. It shows that drones can contribute to a quick and structured data collection, particularly for large building stocks, and thus complement current approaches for district-scale analysis. However, the significant distance between the drone camera and building, which is necessary for automated flight routes, and varying recording angles limit the quantitative interpretability of thermographic images. Therefore, innovative approaches were developed to process image datasets generated using drones. A newly designed AI-based approach can automate the detection of thermal bridges on rooftops. Using generalizations about certain building classes as demonstrated by buildings from the 1950s and 1960s, a novel interpretation method for drone images is suggested. It enables decision-making regarding the need to retrofit thermal bridges of recorded buildings. A novel optimization model for German single-family houses was developed and applied in a case study to investigate the financial and ecological benefits of different thermal retrofit measures. The results showed that the retrofitting of building façades can significantly save energy. However, they also revealed that replacing the heating systems turns out to be more cost-effective for carbon dioxide savings.
Small datasets, limited availability of technical equipment, and the need for simplified assumptions for building characteristics without any information were the main challenges of the approaches in this dissertation.