||DATA ENVIRONMENTS AND PROCESSING IN SEMI-AUTOMATED SIMULATION WITH ENERGYPLUS
||B Vladimir, T Maile, J T. O’Donnell, C M Rose, N Mrazovi_
||Building energy performance (BEP) simulation is increasingly used worldwide to quantitatively justify building design decisions and building operations strategies. It is becoming increasingly obvious that the results of such simulation are often questionable, cannot be trusted, and may lead to wrong decisions. Poor simulation model definition and the use of inappropriately acquired and transformed data are two of the most common causes of this. The use of LBNL methodology for semi-automated BEP simulation data input automates data acquisition and transformation, which removes human decision making from the simulation input data definition process. The first of the three major software components (the Geometry Simplification Tool or GST) is already in use. Work on the second component (an interoperable HVAC graphic user interface for EnergyPlus) is under development. The third component (an internal loads generation tool) will be developed in the near future. The original HVAC GUI for EnergyPlus component has evolved into a BEP simulation platform code-named Mojito. A new internal data model which defines all object/attribute/ relationship sets used in BEP simulation, called SimModel, is the central feature of Mojito. Modeling imprecision is very characteristic of geometry representation in building models submitted by the Architecture-Engineering-Construction-Owners-Operator (AECOO) industry. This, and the lagging and very slow development of CAD utilities that can generate higher-level space boundaries needed in BEP simulation, has forced the development of a new tool (SBT) that calculates higher-level space boundaries from IFC-compliant definition of basic building geometry from any model-based CAD tool. It has also forced the addition of new data transformation rules in GST. This paper describes the principles and high-level views of SimModel, SBT and GST internal architectures, and discusses some of the model and tool functionalities. It also provides a brief summary of quality assessment characteristic of building models generated in the AECOO industry.
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||Building data, semi-automated simulation, simulation software, energy simulation data model, data transformation.
B Vladimir, T Maile, J T. O’Donnell, C M Rose, N Mrazovi_ (2011).
DATA ENVIRONMENTS AND PROCESSING IN SEMI-AUTOMATED SIMULATION WITH ENERGYPLUS. Proceedings of the 28th International Conference of CIB W78, Sophia Antipolis, France, 26-28 October,