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Markus Schaffer specializes in developing innovative approaches that leverage commercial smart heat water meter data to define building-specific actions for renovation projects. His work focuses on transforming raw data from smart water meters into reliable and useful datasets while integrating existing building information. Markus created a high-quality, unique dataset that significantly supports the scientific community, and developed automated techniques to disaggregate smart meter readings to differentiate between space heating and domestic hot water demands at a city level. Additionally, he utilizes smart water meter data to detect building occupancy in a non-intrusive manner on a large scale. His research includes exploring innovative renovation concepts for schools, emphasizing the integration of double-skin facades and diffuse ceiling ventilation, while assessing the performance of these concepts through parametric building performance simulations. Apart from his primary research, Markus applies his expertise in machine learning to assist colleagues in detecting occupants based on Indoor Environmental Quality sensor data. He teaches master's-level courses in System Development, Modelling, and Design Optimization, integrating various subjects such as visual programming, passive design strategies, and Zero Emission Building concepts.
Aalborg University • Aalborg, Denmark
Teaching and researching in the fields of building performance, smart meter data utilization, and machine learning applications in the built environment.
Requirements apply generally to Master's programs across departments including Sociology, Business, and Engineering at Aalborg University.