A Parallel World Framework for scenario analysis in knowledge graphs
Autor: | Arkadiusz Chadzynski, Mei Qi Lim, Eddy Y. S. Foo, Sebastian Mosbach, Laura Ong, Aravind Devanand, Gourab Karmakar, Raymond Lau, Kevin Aditya, Andreas Eibeck, Markus Kraft, Iftekhar A. Karimi |
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Přispěvatelé: | Mosbach, Sebastian [0000-0001-7018-9433], Kraft, Markus [0000-0002-4293-8924], Apollo - University of Cambridge Repository |
Rok vydání: | 2020 |
Předmět: |
Computer science
Distributed computing Interoperability Complex system 02 engineering and technology Linked data scenario analysis Grid Domain (software engineering) Set (abstract data type) 020401 chemical engineering knowledge graph parallel world 020204 information systems superstructure versioning 0202 electrical engineering electronic engineering information engineering Semantic Web Stack 0204 chemical engineering Software versioning |
DOI: | 10.17863/cam.55530 |
Popis: | This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture. |
Databáze: | OpenAIRE |
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