Bygningsdesign DE FØRSTE PRODUAL PROXIMA™ -PRODUKTER Let the data tell you the truth www.produal.dk/pump Data-driven decision support for high-performance building design: Towards evidence-based design decisions using knowledge discovered in BIM data repositories and operational building data By Ekaterina Petrova, PhD Student, Department of Civil Engineering, Aalborg University The advances in design practices utilizing Building Information Modelling (BIM) and Building Performance Simulations (BPS) make it possible to address the high requirements towards overall performance. BIM allows integration of multidisciplinary information within building models and information exchange with various simulation tools. That is fundamental in high-performance design, which requires optimization according to multiple criteria and multidisciplinary input. Furthermore, the demands towards energy efficiency, comfort, health, and productivity are constantly becoming higher. Yet, despite the development in technology and advanced analytical approaches, gaps in building performance still persist. Inaccurate assumptions about input parameters easily lead to inaccurately predicted building performance. Analyti- cal models used during design are also rarely revisited during building operation. That means that the design assumptions are not modified according to the actual performance. Additionally, many of the decisions taken during the building design process are based on rules of thumb and previous experience (tacit knowledge), but not on sound evidence. This often leads to repeated errors and performance gaps, which could have been easily avoided. The richness of measured operational building data makes it possible to analyse the actual performance and explain the origin of many issues. Powerful techniques such as Big Data analytics, machine learning, semantic query techniques, etc. make the prediction of outcomes possible and much more accurate. These approaches can directly influence the decisionmaking process in the industry and make it evidence-based. The purpose of this article is to identify the relevant data sources in existing projects and buildings in operation, how information can be harvested, what knowledge can be “brought back” to the design team in future projects, and how it can improve the deci- sion-making in the building design process. Data synergy in highperformance building design Advanced BIM practice advises the use of a Common Data Environment (CDE) to manage the multidisciplinary information. That includes data that is often not captured in a BIM model (e.g. client requirements, point cloud data, simulation results, etc.). The CDE is defined as a central repository containing project information from all stakeholders. That information is not limited to Figure 1. Use of a Common Data Environment in collaborative design. 28 HVAC 6 · 2018 The indoor climate company
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