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Dynamic control of district heating networks with integrated emission modelling:A dynamic knowledge graph approach

Energy and AI 2024年第3期17卷 29-47页

作者： Markus Hofmeister Kok Foong Lee Yi-Kai Tsai Magnus Müller Karthik Nagarajan Sebastian Mosbach Jethro Akroyd Markus Kraft Department of Chemical Engineering and Biotechnology University of CambridgePhilippa Fawcett DriveCambridgeCB30ASUnited Kingdom Cambridge Centre for Advanced Research and Education in Singapore CARES Ltd.1 Create WayCREATE Tower#05-05138602Singapore cmcl innovations Sheraton HouseCambridgeCB30AXUnited Kingdom The Alan Turing Institute 96 Euston RoadLondonNW12DBUnited Kingdom

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

关键词： Knowledge graph Digital twin Inter operability Energy modelling Emi ssion dispersi on

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