A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling. (15th August 2018) - Garwood, Tom Lloyd; Hughes, Ben Richard; O'Connor, Dominic; Calautit, John K.; Oates, Michael R.; Hodgson, Thomas
Highlights: Improved Building Energy Modelling workflow proposed for existing buildings. Solution proposed for rapid generation of as-built geometry from Point Clouds. Identification of a framework for storing the building geometry in gbXML format. Plans for future verification of solution outlined using industrial standards. Abstract: The industrial sector accounts for 17% of end-use energy in the United Kingdom, and 54% globally. Therefore, there is substantial scope to accurately simulate and efficiently assess potential energy retrofit options for industrial buildings to lower end use energy. Due to potentially years of facility renovation and expansion Building Energy Modelling, also called Building Energy Simulation, applied to industrial buildings poses a complex challenge; but it is an important opportunity for reducing global energy demand especially considering the increase of readily available computational power compared with a few years ago. Large and complex industrial buildings make modelling existing geometry for Building Energy Modelling difficult and time consuming which impacts analysis workflow and assessment options available within reasonable budgets. This research presents a potential framework for quickly capturing and processing as-built geometry of a factory, or other large scale buildings, to be utilised in Building Energy Modelling by storing the geometry in a green building eXtensible Mark-up Language (gbXML) format, which is compatible with most commercially available Building Energy Modelling tools. Laser scans were captured from the interior of an industrial facility to produce a Point Cloud. The existing capabilities of a Point Cloud processing software and previous research were assessed to identify the potential development opportunities to automate the conversion of Point Clouds to building geometry for Building Energy Modelling applications. This led to the novel identification of a framework for storing the building geometry in the gbXML format and plans for verification of a future Point Cloud processing solution. This resulted in a sample Point Cloud, of a portion of a building, being converted into a gbXML model that met the validation requirements of the gbXML definition schema. In conclusion, an opportunity exists for increasing the speed of 3D geometry creation of existing industrial buildings for application in BEM and subsequent thermal simulation.