Periodic Reporting for period 1 - WILSON (Distributed data modelling and Federated Digital Twinning for lifecycle data-driven sustainable operation and management of buildings and districts)
Berichtszeitraum: 2024-05-01 bis 2025-10-31
As the built environment continues to face challenges such as climate change, energy consumption, and social inequality, there is a growing need for adopting sustainable building practices beyond energy considerations, encompassing environmental, social, and economic dimensions across the entire lifecycle of buildings.
In view of these challenges, WILSON proposes a groundbreaking approach, leveraging semantic data repositories, cognitive digital twins, and decentralized data management to transform building and buildings’ portfolio management. The project aims to enhance sustainability by harnessing cutting-edge technologies and innovative management practices. To achieve it, WILSON main development is focused on a decentralized data mesh architecture that is agnostic to and interoperable with proprietary BMS and Digital Twin Systems.
Key objectives include improving building operations, evaluating environmental impact, and enhancing building diagnosis and monitoring. Personalized Data Hubs (PDHs) will connect and integrate all WILSON solutions, adhering to International Data Space (IDS) principles. These solutions will be complemented by an Investment Tool to support decision-making on renovation and RES investments.
To maximize its impact across the EU, WILSON will validate and demonstrate its solutions in four large-scale pilots across different European countries. The consortium comprises 16 partners (50% SMEs and industry) with diverse expertise, including research institutions, technological providers, an international Data Association, and finance SMEs. Overall, the project aims to upgrade 200,000 m2 of buildings, reducing maintenance costs by up to 35%. In the long- term, WILSON seeks to ensure Europe's global competitiveness, data sovereignty, and achievement of EU energy and environmental goals by 2050, resulting in a highly energy-efficient and decarbonized building stock.
In view of these challenges, WILSON proposes a groundbreaking approach, leveraging semantic data repositories, cognitive digital twins, and decentralized data management to transform building and buildings’ portfolio management. The project aims to enhance sustainability by harnessing cutting-edge technologies and innovative management practices. To achieve it, WILSON main development is focused on a decentralized data mesh architecture that is agnostic to and interoperable with proprietary BMS and Digital Twin Systems.
Key objectives include improving building operations, evaluating environmental impact, and enhancing building diagnosis and monitoring. Personalized Data Hubs (PDHs) will connect and integrate all WILSON solutions, adhering to International Data Space (IDS) principles. These solutions will be complemented by an Investment Tool to support decision-making on renovation and RES investments.
To maximize its impact across the EU, WILSON will validate and demonstrate its solutions in four large-scale pilots across different European countries. The consortium comprises 16 partners (50% SMEs and industry) with diverse expertise, including research institutions, technological providers, an international Data Association, and finance SMEs. Overall, the project aims to upgrade 200,000 m2 of buildings, reducing maintenance costs by up to 35%. In the long- term, WILSON seeks to ensure Europe's global competitiveness, data sovereignty, and achievement of EU energy and environmental goals by 2050, resulting in a highly energy-efficient and decarbonized building stock.
During the first reporting period, WILSON delivered substantial technical progress towards an interoperable, data-driven ecosystem for building and district Digital Twins.
The project defined detailed technical requirements, use cases and KPIs, together with a comprehensive mapping of pilot assets, data availability and gaps. Eight representative use cases were specified to link digital twin tools and business models with pilot and stakeholder needs, providing a common framework for subsequent development and demonstration. In parallel, the BIM-SRI tool architecture was designed, including an IFC-to-SRI mapping framework and automated SRI calculation workflows aligned with relevant standards and national baselines.
A key achievement is the definition and staged deployment of the WILSON Data Space, including identity management, federated catalogues and connectors enabling secure, sovereign and interoperable data exchange. On this basis, a curated ontology stack and a minimum viable semantic Digital Twin model were developed and demonstrated on a real pilot, integrating BIM, IoT, BMS and maintenance data. Federation mechanisms were introduced to scale from building- to district-level Digital Twins using geospatial modelling and SPARQL-based querying.
The first version of the Personalised Data Hub (PDH) was engineered using the BIM model of the CIRCE Living Lab, together with IFC and CSV connectors and an alpha peer-to-peer marketplace prototype based on decentralised access control principles. Data-based energy solutions were validated in a living lab environment, including enhanced BMS concepts, a digital building logbook and building/district energy optimisation methods. Additional developments addressed predictive maintenance, environmental and resilience assessment, LCA/LCC integration and material passports for circular renovation, all aligned with the latest European regulations and standards.
The project defined detailed technical requirements, use cases and KPIs, together with a comprehensive mapping of pilot assets, data availability and gaps. Eight representative use cases were specified to link digital twin tools and business models with pilot and stakeholder needs, providing a common framework for subsequent development and demonstration. In parallel, the BIM-SRI tool architecture was designed, including an IFC-to-SRI mapping framework and automated SRI calculation workflows aligned with relevant standards and national baselines.
A key achievement is the definition and staged deployment of the WILSON Data Space, including identity management, federated catalogues and connectors enabling secure, sovereign and interoperable data exchange. On this basis, a curated ontology stack and a minimum viable semantic Digital Twin model were developed and demonstrated on a real pilot, integrating BIM, IoT, BMS and maintenance data. Federation mechanisms were introduced to scale from building- to district-level Digital Twins using geospatial modelling and SPARQL-based querying.
The first version of the Personalised Data Hub (PDH) was engineered using the BIM model of the CIRCE Living Lab, together with IFC and CSV connectors and an alpha peer-to-peer marketplace prototype based on decentralised access control principles. Data-based energy solutions were validated in a living lab environment, including enhanced BMS concepts, a digital building logbook and building/district energy optimisation methods. Additional developments addressed predictive maintenance, environmental and resilience assessment, LCA/LCC integration and material passports for circular renovation, all aligned with the latest European regulations and standards.
During the first reporting period, WILSON has delivered the essential technical and organisational groundwork required to achieve its expected impacts. The consortium developed the initial WILSON Data Space, early versions of Digital Twin models and Personalised Data Hubs, and a comprehensive KPI framework aligned with the Grant Agreement. These results enable harmonised data acquisition, semantic interoperability and performance assessment across the four demonstrators. Stakeholder engagement was significantly advanced through synergies with 13 EU projects, participation in major sector events and activation of a multi-channel communication and dissemination strategy, strengthening the project’s integration within the Built4People community. These achievements demonstrate clear potential for scientific impact (advancement of federated data architectures and DT methodologies), economic and industrial impact (foundations for new data-driven services and digital building management solutions), and environmental and societal impact (future improvements in energy performance, comfort, maintenance and circularity).
To ensure successful uptake in the next phases, some key needs have been identified. Full demonstration activities must be deployed and continuously monitored to validate KPIs and quantify impacts under real operational conditions. Exploitation planning should further support partners in positioning the PDHs, data-space connectors and optimisation tools for market deployment and access to finance. Continued alignment with regulatory and standardisation developments, particularly on interoperability, data governance and the evolving Energy Performance of Buildings Directive (EPBD) and Smart Readiness Indicator (SRI) frameworks, will be essential to maximise relevance, replicability and adoption. Maintaining strong collaboration with related initiatives and stakeholders will also be important to translate technical and methodological results into widely applicable solutions and policy-relevant insights.
To ensure successful uptake in the next phases, some key needs have been identified. Full demonstration activities must be deployed and continuously monitored to validate KPIs and quantify impacts under real operational conditions. Exploitation planning should further support partners in positioning the PDHs, data-space connectors and optimisation tools for market deployment and access to finance. Continued alignment with regulatory and standardisation developments, particularly on interoperability, data governance and the evolving Energy Performance of Buildings Directive (EPBD) and Smart Readiness Indicator (SRI) frameworks, will be essential to maximise relevance, replicability and adoption. Maintaining strong collaboration with related initiatives and stakeholders will also be important to translate technical and methodological results into widely applicable solutions and policy-relevant insights.