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.
In this paper, we demonstrate through
examples how the concept
of a Semantic Web based knowledge graph can be used to integrate combustion
modeling into cross-disciplinary applications and in particular how
inconsistency issues in chemical mechanisms can be addressed. We discuss
the advantages of linked data that form the essence of a knowledge
graph and how we implement this in a number of interconnected ontologies,
specifically in the context of combustion chemistry. Central to this
is OntoKin, an ontology we have developed for capturing both the content
and the semantics of chemical kinetic reaction mechanisms. OntoKin
is used to represent the example mechanisms from the literature in
a knowledge graph, which itself is part of the existing, more general
knowledge graph and ecosystem of autonomous software agents that are
acting on it. We describe a web interface, which allows users to interact
with the system, upload and compare the existing mechanisms, and query
species and reactions across the knowledge graph. The utility of the
knowledge-graph approach is demonstrated for two use-cases: querying
across multiple mechanisms from the literature and modeling the atmospheric
dispersion of pollutants emitted by ships. As part of the query use-case,
our ontological tools are applied to identify variations in the rate
of a hydrogen abstraction reaction from methane as represented by
10 different mechanisms.
This Article illustrates how a dynamic
knowledge graph approach
in the context of The World Avatar (TWA) project can support the decarbonization
of energy systems by leveraging the existing energy storage system
(ESS) selection framework to assist in the selection and optimal placement
of the ESS. TWA is a dynamic knowledge graph based on the Semantic
Web and its associated technologies, with intelligent agents operating
on it. The agents act autonomously to update and extend TWA, and thus
it evolves in time. TWA also provides the ability to consider different
scenarios, referred to as parallel worlds, allowing for scenario analysis
without mutual interference. A use casethe addition of a battery
energy storage system to the Singapore Jurong Island electrical networkis
introduced to demonstrate the application of this approach. The domain
ontology, OntoPowSys, was extended to describe and instantiate the
relevant ESSs considered in the use case. This extension is described
in the Article using the description logic syntax. The Article also
outlines the details of how the various agents involved in the use
case are being integrated into TWA. The use case also highlights how
the parallel world framework can facilitate scenario analysis by considering
different scenarios without affecting the real-world representation.
Singapore's urban planning and management is crossdomain in nature and need to be assessed using multi-domain indicators -such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation.
Urban planning relies on the definition, modelling and evaluation of multidimensional phenomena for informed decision-making. Urban building energy modelling, for instance, usually requires knowledge about the energy use profile and surface area of each use that takes place within a building. We do not have a detailed understanding of such information for mixed-use developments, which are gaining prominence in urban planning. In this paper, we developed a methodology to quantitatively define the characteristics of mixed-use developments using archetypes of programme profiles (ratios of each programme type) of a city’s mixed-use plots. We applied our methodology in Singapore, resulting in 163 mixed-use zoning archetypes using Singapore’s master plan data and Google Maps API data. In a case study, we demonstrated how these archetypes can be used to provide more detailed data for urban building energy modelling, including energy demand forecasts and energy supply system design. To enable future automation of the workflow, the archetype definitions were represented and stored as a machine-readable ontology. This ontology can later be extended with for example, the mobility properties of archetypes; thus, enabling the archetypes' use in other urban planning applications beyond building energy modelling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.