Faculty of Computer Science

Research Group Theoretical Computer Science

Oberseminar: Heterogene formale Methoden

Abstract:

Unless we reverse the trend, our planet is on track to warm by two to four degrees Celsius by the end of the century. We are approaching the deadline while still experimenting with new ways to respond to potential changes and growing uncertainties. In climate and energy modeling, assumptions regarding the future of the variables of interest, such as greenhouse gas emissions or renewable energy shares, are presented in the form of a scenario developed by a group of researchers. While scenarios cannot predict the future, they can outline what is required (in terms of technologies and investments) to ensure a brighter future. Currently, comparing scenarios from several research projects has to be carried out manually and in a time-consuming manner. The primary goal of the SIROP project is to (partially) automate scenario comparisons. During this presentation, we will go over the specifics of qualitative and quantitative scenario comparisons using Open Energy Ontology (OEO), scenario factsheets, and the generated knowledge graph. We will also discuss plans for visualizing the outcomes of the comparisons. In addition, we will talk about how to use machine learning approaches to automate the processof knowledge graph generation and to enable a semantic recommender system for exploratory search in the energy domain.