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Abstract 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)… Click to show full abstract
Abstract 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.