AI and Digital Twins for Flexible Energy Systems
By professor Jiri Sbra, AAU
The recent surge in electricity prices has increased the demand for cost-effective and sophisticated controllers for domestic heating. I will survey a few recent projects at the CS department of Aalborg University that use reinforcement learning techniques implemented in the tool UPPAAL Stratego to improve the comfort and energy-efficiency of heating in family houses. We shall see how to improve the performance of floor heating systems as well as heat pumps in combination with heat tanks. The research ranges from establishing sound theoretical foundations, to simulations and experiments on digital twins as well as on real family houses.

Total resource accounting for transparent carbon emission reporting
By professor Fritz Henglein, KU
Increasing pressure to mitigate CO2 emissions by EU regulation and private interests require trustworthy registration, processing and modeling of production and transportation processes across deep supply chains to provide trustworthy provenance of resources and their climate impact upstream and downstream.

We present a decentralized modeling and accounting framework, total resource accounting, that prevents digital greenwashing by ensuring that environmentally certified products cannot be produced out of uncertified ingredients, components and energy sources nor disappear in an unaccounted fashion. We illustrate its applicability to fine-grained tracking of primary electricity production in sector-coupled energy transformation, and transparent carbon emission calculation as a verifiable basis for green certificate of origin issuance, carbon emission offsetting and carbon credit trading.