Search

The adoption of heat pumps to displace the use of gas for domestic heating is a major component of the strategy to reduce emissions in the UK. This study examines the impact of adopting heat pumps on regional inequalities in the UK. An index is used to assess how variations in household fuel costs could affect regional disparities across different future price scenarios. The findings reveal that, at 2019 prices, most households would face higher heating costs with heat pumps. However, following the 2022 energy price shock, heat pump adoption would lead to lower heating costs for most households compared to gas heating. The effect is sensitive to the electricity-to-gas price ratio, with regions experiencing high fuel poverty being most vulnerable to negative impacts. By mapping these geospatial effects, the study enables the forecasting of future inequality trends, providing insights for informed policy development. The results suggest that, under appropriate price structures, heat pump adoption could contribute to both decarbonisation and reduced social inequality. An example mechanism for financial support to mitigate the impact of adopting heat pumps on inequality is demonstrated. This study highlights the novel capability of The World Avatar (TWA) approach to integrate cross-domain data sets, combining energy policy with social equity goals. By forecasting future inequality trends based on energy price scenarios, the study provides a route to valuable insights to support informed policy development, highlighting how the adoption of heat pumps can influence regional inequalities and emphasising the need for targeted interventions to support vulnerable regions.

This paper presents a dynamic knowledge graph approach that offers a reusable, interoperable, and extensible framework for modelling power systems. Domain ontologies have been developed to support a linked data representation of infrastructure data, socio-demographic data, areal attributes like demand, and models describing power systems. The knowledge graph links the data with a hierarchical representation of administrative regions, supporting geospatial queries to retrieve information about the population within the vicinity of a power plant, the number of power plants, total generation capacity, and demand within specific areas. Computational agents were developed to operate on the knowledge graph. The agents performed tasks including data uploading, updating, retrieval, processing, model construction and scenario analysis. A derived information framework was used to track the provenance of information calculated by agents involved in each scenario. The knowledge graph was populated with data describing the UK power system. Two alternative models of the transmission grid with different levels of structural resolution were instantiated, providing the foundation for the power system simulation and optimisation tasks performed by the agents. The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

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.

Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.

Abstract The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist’s research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA’s potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning.

This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.

Today, technological developments are ever-growing yet fragmented. Alongside inconsistent digital approaches and attitudes across city administrations, such developments have made it difficult to reap the benefits of city digital twins. Bringing together experiences from five research projects, this paper discusses these digital twins based on two digital integration methodologies—systems and semantic integration. We revisit the nature of the underlying technologies, and their implications for interoperability and compatibility in the context of planning processes and smart urbanism. Semantic approaches present a new opportunity for bidirectional data flows that can inform both governance processes and technological systems to co-create, cross-pollinate, and support optimal outcomes. Building on this opportunity, we suggest that considering the technological dimension as a new addition to the trifecta of economic, environmental, and social sustainability goals that guide planning processes, can aid governments to address this conundrum of fragmentation, interoperability, and compatibility.

This paper introduces an extension of a previously developed question answering (QA) system for chemistry, operating on a knowledge graph (KG) called Marie. This extension enables the automatic invocation of semantic agents to answer questions when static data is absent from the KG. The agents are semantically described using the agent ontology, OntoAgent, to enable automated agent discovery and invocation.The natural language processing (NLP) models of the QA system need to be trained in order to interpret questions to be answered by new agents. For this purpose, we extend OntoAgent so that it becomes possible to automatically create training material for the NLP models.We evaluate the extended QA system with two example chemistry-related agents and an evaluation question set. The evaluation result shows that the extension allows the QA system to discover the suitable agent and to invoke the agent by automatically constructing requests from the semantic agent description, thereby increasing the range of questions the QA system can answer.

This paper presents a system of autonomous intelligent software agents, based on a cognitive architecture, capable of automated instantiation, visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs. Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability. Development of CityImportAgent, CityExportAgent and DistanceAgent showcased intelligent automation capabilities of the Cities Knowledge Graph. The agents successfully created a semantic model of Berlin in LOD 2, compliant with CityGML 2.0 standard and consisting of 419 909 661 triples described using OntoCityGML. The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph. This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar.

This paper investigates how using heat pumps for domestic heating would impact fuel poverty and social inequality. The analysis integrates a geospatial description of climate observations, gas and electricity infrastructure, energy consumption and fuel poverty from the base world of a Universal Digital Twin based on the World Avatar knowledge graph. Historic temperature data were used to estimate the temporal and geospatial variation of the performance of air source heat pumps in the UK. The corresponding change in gas and electricity consumption that could be achieved using heat pumps instead of gas for domestic heating was estimated. The geospatial impact of the heat pumps was assessed in terms of CO2 savings, and their effect on fuel cost and fuel poverty. Whilst heat pumps would reduce emissions, it is predicted that they would increase fuel costs. It was shown that both local and regional areas of high fuel poverty would experience some of the largest increases in fuel cost. This illustrates the potential for the transition to sustainable heating to exacerbate social inequality. The analysis suggests that existing regional inequalities will increase, and that it comes down to a political choice between investments to support the most effective use of heat pumps, and delayed investments to counter social inequality. The ability of the World Avatar to integrate the models and data necessary to perform this type of holistic analysis provides a means to generate actionable information, for example, to enable local policy interventions to address the tension between social and environmental goals.

This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customized vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in two use cases that show the potential of the Universal Digital Twin to share data across sectors. The first use case combines data about land use with a geospatial analysis of scenarios for energy provision. The second illustrates how the Universal Digital Twin could use the land use data to support the cross-domain analysis of flood risk. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed.

This article applies a knowledge graph-based approach to unify multiple heterogeneous domains inherent in climate and energy supply research. Existing approaches that rely on bespoke models with spreadsheet-type inputs are noninterpretable, static and make it difficult to combine existing domain specific models. The difficulties inherent to this approach become increasingly prevalent as energy supply models gain complexity while society pursues a net-zero future. In this work, we develop new ontologies to extend the World Avatar knowledge graph to represent gas grids, gas consumption statistics, and climate data. Using a combination of the new and existing ontologies we construct a Universal Digital Twin that integrates data describing the systems of interest and specifies respective links between domains. We represent the UK gas transmission system, and HadUK-Grid climate data set as linked data for the first time, formally associating the data with the statistical output areas used to report governmental administrative data throughout the UK. We demonstrate how computational agents contained within the World Avatar can operate on the knowledge graph, incorporating live feeds of data such as instantaneous gas flow rates, as well as parsing information into interpretable forms such as interactive visualizations. Through this approach, we enable a dynamic, interpretable, modular, and cross-domain representation of the UK that enables domain specific experts to contribute toward a national-scale digital twin.

This paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted.

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.

We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NOx emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.