Technical Report 319, c4e-Preprint Series, Cambridge

Authors: Markus Hofmeister, Kok Foong Lee, Yi-Kai Tsai, Magnus Müller, Karthik Nagarajan, Sebastian Mosbach, Jethro Akroyd, and Markus Kraft*

Reference: Technical Report 319, c4e-Preprint Series, Cambridge, 2024

Associated Themes:
 

• Implement knowledge graph native model predictive control-style optimisation.
• Connect energy and air quality domain with integrated emission dispersion modelling.
• Embed general forecasting capabilities within a dynamic knowledge graph.
• Develop domain ontologies to represent district heating operations data.

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 set of available heat sources, and couple it with dispersion modelling of corresponding emissions to provide automatic insights into air quality implications of various heat sourcing strategies. We achieve cross-domain insights in the nexus of energy and air quality via a set of developed ontologies and autonomous software agents, which can be chained together via the World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we have integrated the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and generation potential to foster strategic analyses and scenario planning. Utilising actual instantiated building and weather data, this enhanced bottom-up version addresses 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.

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