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Start of funding 01.01.2024

AI-Enhanced and data driven estimator for carbon-neutral operated buildings

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This collaborative endeavor seeks to develop an AI-enhanced, data-driven estimator tailored for building owners. The primary objective is to provide building owners with a strategic array of feasible enhancements, effectively reducing the carbon footprint of their existing buildings. The estimator employs a holistic approach, considering aspects such as energy demand and supply, storage possibilities, and potential enhancements to the building. By integrating contemporary AI methods with both measured and synthetically generated building data, the collaboration aims to derive optimized solutions. The ultimate goal is to guide building owners towards achieving substantial reductions in carbon emissions for their buildings.

Final report:
Within this project, two research stays in California were conducted to establish a transatlantic collaboration on AI-enhanced, data-driven methods for building decarbonization. Key activities included guest lectures and discussions conducted within the framework of a seminar. In addition, in-depth scientific exchanges were held with six PhD students from the research group of Prof. Fischer. Several coordination meetings with the involved professors contributed to the development of a shared research perspective. Concrete synergies between the project and ongoing doctoral research were identified, and the integration of microgrids was defined as a key component of the research approach.

A central transfer outcome is the elective course “Digital Tools for Building Decarbonization”, which was successfully offered at Technische Hochschule Augsburg in the summer semester 2025. The course served as a bridge between international research, teaching, and early-career researcher development. The resulting methodological and didactic insights are currently being incorporated into a joint scientific publication. Furthermore, the partners agreed to support future joint research proposals through Letters of Intent (LoIs). The long-term objective is the development of follow-up third-party funded projects, aiming at the funding of a joint PhD position.