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Steadily increasing use of Building Information Modelling (BIM) in all phases of building's lifecycle, together with more attention for openBIM and growing software support for the most recent version of the Industry Foundation Classes (IFC 4) have created a very promising context for an even broader application of Building Energy Performance Simulation (BEPS). At the same time, an urgent need for modelling guidelines and standardisation becomes evident. A well-defined BIM-based workflow and a set of tools that fully exploit and extend the possibilities of the openBIM-technology can make the difference when it comes to reliability and cost of BEPS to design, build and operate high-performance buildings. This paper describes the essential elements of this integrated workflow, explains why openBIM comprises much more than just a standardized file-format and what is achieved with the already available technology, namely the Information Delivery Manual (IDM) and a newly developed Model View Definition. This MVD is tailored to the needs of Building Energy Performance Simulation (BEPS) that uses the Modelica language together with a specific library (IDEAS) and can easily be adapted to other libraries. In this project, several tools have been developed to closely integrate BEPS and IFC4. The simulation engine now gets the vast majority of the required input directly from the IFC4-file. For the implementation of the tools, the PYTHON language and the open source library IfcOpenShell are used. A case study is presented, that was used for extensive tests of the proposed approach and the implemented tools. The essential benefits of this new workflow are illustrated, and the feasibility is demonstrated. Opportunities and remaining bottlenecks are identified to encourage further development of BIM software to fully support IFC4 as an information source for BEPS. Beside some improvements of the proprietary class structure and functionality, enabling the export of IFC4 files based on custom MVDs is one required key feature.

A large part of energy usage in buildings occurs during the operational phase, emphasising the need for efficient and improved facility management, operation and control. Model Predictive Control (MPC) or Fault Detection and Diagnosis (FDD) are among the strategies that allow minimising energy use and costs during operation. However, the need for fast and accurate dynamic models (e.g. grey box model), which are time-consuming and challenging to implement, precludes their systematic integration in the built environment. A typical grey-box modelling approach consists of manually implementing several grey-box model structures with an increasing level of complexity before performing a forward selection procedure to identify the optimal configuration. The link between the different grey-box models and the monitored data is also established manually. Such an approach can be both time-consuming and error-prone and involves a significant cost that hampers the broad adoption of strategies such as MPC and FDD. This study proposes a tool-chain that uses BIM to automatically generate several grey-box structures with added complexities stemming from the specific geometry and design of the building. More specifically, an existing rule-based IFC to Modelica interface is extended to automatically create several Modelica-based grey box models that gradually take into account the building's specific information and characteristics. Additional rules are also proposed to automate the connection between the models and the building monitoring system. As a forward selection approach, a multi-objective optimisation using the NSGA-2 algorithm is adopted. The application of the tool-chain on two case studies shows that the integration of BIM to automate the implementation of grey box models, not only reduces the human involvement in the modelling process but can also produce more accurate models. Besides, this study shows that the use of multi-objective optimisation with datasets from two different seasons results in models that are valid for all seasons.

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