Imagine if every building had a digital passport that kept all its energy and maintenance records in one place instead of in separate folders. This project creates a smart system that cleans up messy building data and turns it into a digital twin. It helps owners see exactly where they are wasting energy so they can fix it without guessing.
Building data is currently trapped in disconnected silos, making it impossible for owners to accurately monitor energy performance or plan renovations. This leads to risky investments and failure to meet decarbonisation targets.
A cloud-based toolbox featuring Digital Building Logbooks, AI-driven analytics, and Digital Building Twins. It includes a blockchain-based framework for data integrity and a data warehouse for unified querying.
If you are a property manager dealing with fragmented energy data across multiple sites — this project developed a cloud-based toolbox that merges different data sources into a digital twin. This allows for better-informed decision-making for performance monitoring and de-risking investments.
If you are a renovation firm dealing with inaccurate building records — this project developed a Digital Building Logbook system. This ensures high-quality data is used to align renovation strategies with decarbonisation requirements.
If you are a software developer dealing with incompatible building data formats — this project developed an interoperable data space based on FIWARE and GAIA-X. This provides a foundation for creating AI-based analytics for the built environment.
Based on available project data, specific pricing or licensing costs are not mentioned as the project focuses on providing an open, interoperable toolbox.
Yes, the system has been deployed across 10 real-world conditions to validate its performance in actual building environments.
Based on available project data, the project utilizes open standards and common EU initiatives like FIWARE, GAIA-X, and IDSA, though specific IP agreements are not detailed.
The tools help align long-term renovation strategies with the Energy Performance of Buildings Directive (EPBD) requirements for decarbonisation.
It uses ETL processes and a specific ontology to convert raw, heterogeneous data into reliable datasets and knowledge graphs.
The project is heavily industry-driven with a 53% industry ratio, comprising 9 industrial partners and 3 SMEs across 11 countries. This strong commercial presence, balanced by 3 universities and 2 research centers, suggests the outputs are designed for practical market application rather than purely theoretical research.
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