ABSTRACT
Thermal energy analyses based on Building Information Models (BIMs) are becoming more and more practicable in architecture, engineering and construction. This enables detailed studies about the building energy behaviour with predefined energy-relevant parameters. Although this is an absolute advantage there are also some problems regarding the daily work of energy experts. The simulation configurations and executions cost much time and the pre-processing can be very erroneous due to design modelling problems, wrong material assignments etc. To allow assignments from external data like product catalogues or climate information to BIM data, the energy-extended BIM (eeBIM) framework was developed as multimodel concept for energy simulations. This multimodel was extended by ontologies to allow semantic enrichments and constraints for checking the model quality of inter-linked models. It can be used as an input data set for thermal energy performance analyses. An energy performance platform, called Virtual Energy Laboratory (VEL), integrates different energy tools and data management functions to allow complex thermal energy simulations based on a BIM and additional energy-relevant data. This paper shows how an optimized Green Building Design (GBD) can automatically be derived from a building information model using semantic technologies and highly-scalable processing methods based on an ontology-controlled workflow in the VEL.
