Prof. Dr. Dr. h. c. Sahin Albayrak is a professor of computer science and holds the chair Agent Technologies in Business Applications and Telecommunication (AOT) at the Technical University of Berlin. He is the founder and head of the Distributed Artificial Intelligence Laboratory (DAI-Labor) at TU Berlin, currently employing over 120 researchers. He is also the founder of Deutsche Telekom Innovation Laboratories and the founding director of the Connected Living Association and the German-Turkish Advanced Research Centre for ICT (GT-ARC). He serves as an advisor to several authorities and companies in both Germany and Turkey. His research interests include distributed systems, machine learning, cybersecurity, multi-agent systems, and autonomous systems, with their particular applications in autonomous driving, smart cities, smart energy systems, new generation telecommunication systems, conversational assistants, and preventive health services.
Smart Energy Pack - AI-enabled autonomous energy supply for small and medium-sized prosumers
At the Distributed Artificial Intelligence Laboratory (DAI-Labor) of the Technische Universität Berlin, we develop autonomous adaptive smart energy management solutions for small and medium-sized prosumers, such as for buildings and districts. We envision an integrated solution with hard- and software to include the consumers into the prosumer-era enabling second-use opportunities for the already installed infrastructure. We have been developing and testing a lab prototype combining power electronics and intelligent control and want to take it out of the lab to provide potential users an integrated solution for households and buildings.
The solution integrates intelligent, modular software with hardware to allow the interaction of end-users with the optimization of local production and storage. We use machine learning to model the uncertainty of expected solar output, user consumption, and mobility behavior, and we use optimization algorithms to increase energy efficiency, decrease CO2 emissions, or reduce costs of the energy supply. The user can configure the expected benefits based on his desires. For that, we test novel use interaction concepts, i.e. multi-modal assistants, to make AI decisions transparent and educate the user to improve environmental awareness.