Explore how the CHRONICLE project bridges BIM, energy modelling, and smart readiness to enable Energy Performance Building Directive-compliant renovation planning, through automated BIM-to-BEP and BIM-to-SRI workflows.
(Note: Opinions in the articles are of the authors only and do not necessarily reflect the opinion of the European Union)
In workflows of the Architecture, Engineering and Construction (AEC) industry, persistent problems lie in the fragmentation of data and tools across disciplines. Architectural models, energy simulations, and smart readiness assessments are often treated as independent processes, leading to redundant modelling efforts, data inconsistencies, and high coordination overhead. For instance, a building may be modelled once in a BIM authoring tool (e.g., Revit or Archicad) for design, again in an energy simulation platform (e.g., DesignBuilder) for performance assessment, and yet again in another tool for evaluating smart readiness. This siloed approach results in inefficiencies and compromises traceability and transparency, making it difficult for stakeholders to maintain a consistent understanding of the building throughout its lifecycle. Furthermore, regulatory demands — especially under frameworks like the EU Green Deal and the revised Energy Performance of Buildings Directive (EPBD; Directive (EU) 2024/1275) — are increasing the pressure to streamline these processes and improve the reliability of data exchanges.
In response, there is a growing need for an integrated, interoperable, and standards-based approach that enables core building data to be seamlessly reused across domains. Standard-aligned data models — specifically, the openBIM format IFC4 format for static data and the BrickSchema ontology for IoT streams — emerge as critical enablers. These models encode geometry, semantics, and systems in a machine-readable and software-neutral way, ensuring consistency across tools and stakeholders. This is especially important for new regulatory instruments like the Smart Readiness Indicator (SRI), which must function independently but in parallel with existing certification frameworks like Energy Performance Certificates (EPCs), often requiring overlapping datasets. Implementing structured transformation pipelines that convert IFC models into simulation-ready or assessment-ready formats for energy and smart readiness workflows reduces redundancy and opens the door to automation. These standards-based data flows do not merely improve data governance — they represent a fundamental shift toward scalable, digital renovation ecosystems.
CHRONICLE, a project funded under the EU Horizon Europe programme, addresses this need by introducing a data-driven, whole-lifecycle building performance ecosystem built on interoperable digital workflows. Central to this ecosystem is the CHRONICLE Common Data Environment (CDE), which facilitates the fusion of BIM and IoT data through the integration of IFC4, BrickSchema, and the Building Topology Ontology (BOT) [1] data models for BIM and IoT data fusion. The CDE serves as a single, authoritative source of structured data supporting a range of downstream applications, including energy simulation and smart readiness evaluation. These capabilities are enabled by transformation templates and mapping rules that support BIM-to-BEP and BIM-to-SRI workflows, ensuring high semantic fidelity while reducing manual input. The data processed through these pipelines is then leveraged by user-oriented tools such as the Integrated Digital Twinning Framework, the Renovation Planner and the Digital Building Logbook, which provide intuitive interfaces for various stakeholder groups.
To operationalise the CHRONICLE vision, a modular architecture, illustrated in Figure 1, has been developed. It integrates both static and dynamic building data into a unified, standards-compliant Common Data Environment. This CDE acts as the digital backbone of CHRONICLE’s lifecycle assessment methodology, coordinating the transformation of heterogeneous datasets into simulation-ready formats, structured evaluation outputs, and dynamic inputs for data-driven and physics-based digital twins (DT).
Figure 1: Overview of the CHRONICLE Common Data Environment (CDE) architecture and workflow.
The components within this pipeline are purpose-built to automate and validate the conversion of building models into inputs for energy performance assessment and smart readiness evaluation, while also supporting enhanced visualisation and decision-making interfaces. Through several external tools and other CHRONICLE platforms interface with this pipeline — such as Revit for BIM authoring, the Renovation Planner for renovation scenarios design and renovation passports issuing, the Digital Building Logbook for lifecycle documentation, and Data-Driven (DD) and Physics-Based Digital Twins for performance simulation, behavioural modelling, and renovation impact analysis — these are considered external to the focus of this article and are therefore not examined in detail. The focus here is on the internal transformation and validation mechanisms that underpin CHRONICLE’s data-driven methodology within the CDE.
At the front end of the process lies the CHRONICLE IFC Model Checker, a key validation component responsible for assessing the completeness, correctness and consistency of building models expressed in the IFC4 schema (static data). Using a dedicated Information Delivery Specification (IDS), this component verifies that the model includes all required properties, classifications, and relationships. The IDS schema codifies expectations for elements such as spatial zones, envelope components, and HVAC systems. The checker flags issues such as missing thermal transmittance values, disconnected system components, or invalid space boundaries, outputting feedback in the form of BCF (BIM Collaboration Format) files that guide authors through corrections within their BIM tools. This iterative loop ensures semantic completeness before simulation or evaluation, reducing errors and increasing downstream automation reliability.
Figure 2: BIM-to-BEP generator – Results of the transformation process for a residence in Greece (left: IFC4, right: EnergyPlus IDF).
Validated models proceed to two parallel transformation engines: the BIM-to-SRI generator and the BIM-to-BEP generator. The BIM-to-SRI generator translates IFC-based building descriptions into the structured input required for calculating the Smart Readiness Indicator, in accordance with the EU’s SRI methodology. This includes detecting and categorising building systems across defined domains — such as heating, cooling, lighting, and automation — and matching them against predefined service catalogues to determine the building’s capacity for smart operation. The generator uses a rule-based inference system to traverse the model’s elements and metadata, assigning scoring weights to the relevant smart functionalities and generating ready-to-use evaluation forms. In parallel, the BIM-to-BEP generator focuses on energy performance modelling by extracting thermophysical properties, zone definitions, construction assemblies, and HVAC topologies from the IFC file. This information is converted into simulation-ready input formats (e.g., EnergyPlus IDF [2]), enabling detailed thermal simulations that reflect the building’s actual geometry and systems. Indicative results of the BIM-to-BEP transformation process for a residential building in Greece are depicted in Figure 2. The inclusion of these two engines ensures that both regulatory tracks — energy performance and smart readiness — can be addressed in an automated, semantically aligned workflow from a single validated source model, making it possible to evaluate candidate renovations in both terms beforehand with speed and fidelity.
To complement static data, CHRONICLE also incorporates dynamic operational data via the DD–DT profiling and obXML generator [3]. This module connects with IoT platforms to capture time-series data streams — including indoor environmental conditions, equipment usage, and occupancy profiles. Advanced behavioural profiling algorithms identify recurring usage patterns, which are encoded in the obXML schema to reflect actual rather than assumed occupant behaviour. These models are passed to the obXML performance evaluation engine, which benchmarks them against thresholds for consistency, temporal density, and plausibility (e.g., correlation between heating patterns and occupancy). Valid models are admitted into the CDE, where they enrich the static dataset with real-world usage dynamics. Importantly, CHRONICLE also ensures that the underlying IoT data streams themselves — not just derived profiles — are persistently stored and accessible within the CDE. This fusion of real-time sensor data with static IFC models creates a comprehensive, multidimensional representation of each building, supporting behavioural calibration of simulations, time-based analytics, and thorough performance evaluation.
At the convergence of these data streams lies one of CHRONICLE’s core innovations: the generation of hybrid data-driven and physics-based digital twins. The physics-based twin simulates deterministic behaviour based on envelope and system specifications, while the data-driven twin calibrates these simulations using actual sensor and behaviour data. These twins offer stakeholders both analytical and empirical lenses on performance, enabling robust scenario evaluation, predictive analytics, and impact assessments for renovation strategies. Data from both twin streams, along with other tools, is fed back into the CDE, establishing it as a unified, bidirectional hub for data exchange, monitoring, and analysis.
Figure 3: CHRONICLE CDE data visualisation in ChroViewFM.
Figure 4: ChroViewOcc.
To support user interaction with the CHRONICLE dataset and make complex performance insights actionable, a suite of data visualisation modules has been developed that builds directly upon the APIs exposed by the Common Data Environment (CDE). These APIs serve as structured endpoints for querying and retrieving both static (e.g., IFC4 models, system topologies) and dynamic (e.g., obXML behaviour profiles, IoT time-series data) building data. Through secure, RESTful web services, the APIs allow authorised tools and users to extract filtered views of building performance, monitor system health, and receive notifications based on threshold violations or sensor anomalies. This infrastructure enables modular integration of visualisation interfaces while ensuring alignment with the semantic models and data integrity mechanisms managed by the CDE.
Among these visualisation tools, ChroViewFM (Figure 3, left) is designed specifically for facility managers and technical professionals tasked with overseeing building operations. This web-based application combines 3D visualisation of the building geometry with real-time data derived from IoT streams. Through its intuitive dashboard, users can monitor key performance indicators such as energy consumption, indoor air quality (e.g., CO₂, temperature, humidity), equipment status, and associated costs or carbon emissions. ChroViewFM also supports drill-down navigation, enabling users to isolate specific building elements for 3D visualisation alongside IoT device data representation for user-defined time periods. Furthermore, since the application is built on web technologies and integrates via the CDE APIs, it provides cross-platform accessibility — allowing remote access to building information from any device or operating system without the need for local software installation.
In parallel, ChroViewOcc (Figure 3, right) targets non-expert users — primarily occupants and tenants — who are typically underserved by technical building dashboards. This mobile application translates building performance and comfort data into clear, intuitive visual summaries that empower residents to understand and manage their indoor environment. Using data exposed via the CDE APIs, ChroViewOcc visualises live indoor conditions such as temperature and air quality, along with historical trends in energy usage. These insights are contextualised using personalised benchmarks and behavioural recommendations, helping occupants interpret their habits and adjust usage patterns to improve efficiency and comfort. The interface has been developed through a user-centred design process with feedback from CHRONICLE pilot sites, focusing on accessibility, simplicity, and trustworthiness. For example, the app avoids jargon and offers colour-coded indicators for smart readiness and energy performance levels.
The CHRONICLE framework demonstrates that a standards-based, interoperable digital infrastructure can effectively overcome the fragmentation that has long hindered data-driven renovation in the built environment. Through integration of static and dynamic data within a unified Common Data Environment, and automation of their transformation into simulation- and assessment-ready formats, CHRONICLE ensures semantic consistency, reduces redundant modelling, and enables timely, trustworthy insights for both professionals and occupants. Through its modular architecture — anchored in IFC4, BrickSchema, and BOT ontologies — and its suite of specialised tools, including BIM-to-BEP and BIM-to-SRI generators, behavioural profiling modules, and dual digital twins, CHRONICLE provides a replicable blueprint for whole-lifecycle building assessment. Its APIs, visualisation modules, and stakeholder-centric interfaces further enhance accessibility and transparency. As buildings face increasing demands for decarbonisation, smart readiness, and user engagement, CHRONICLE stands as an instructive example of how data harmonisation and digital workflows can be scaled to meet regulatory goals while fostering trust and usability across the value chain.
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[2] D. Crawley, L. Lawrie, F. Winkelmann, W. Buhl, Y. J. Huang, C. Pedersen, R. Strand, R. Liesen, D. Fisher, M. Witte and J. Glazer, 'EnergyPlus: creating a new-generation building energy simulation program,' Energy and Buildings, vol. 33, no. 4, pp. 319-331, 2001.
[3] T. Hong, S. D'Oca, S. Taylor-Lange, W. Turner, Y. Chen and S. Corgnati, 'An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema,' Building and Environment, vol. 94, no. 1, pp. 196-205, 2015.
