Towards the Application of Uncertainty Analysis in Architectural Design Decision-Making - A Probabilistic Model and Applications

Shahsavari, Fatemeh; Koosha, Rasool; Vahid, Milad R.; Yan, Wei; Clayton, Mark J.

doi:10.52842/conf.ecaade.2018.1.295

Cited by 1 publication

(3 citation statements)

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“…A model-based energy calculation tool is developed in FORTRAN, with a Graphical User Interface (GUI) in Microsoft Excel. The Resistance-Capacitance modeling method (Shahsavari et al, 2018) is applied to model building physics in a steady state. The outputs of this tool are building annual heating and cooling energy consumption (AHCEC), and deviation from comfort temperature (DCT).…”

Section: Simulationmentioning

confidence: 99%

“…The promising development of BIM technologies encourages the application of probabilistic methods in architectural building design to overcome the uncertainties. Built upon our previous research on the implementation of probabilistic methods in building energy analysis (Shahsavari et al 2018), this paper introduces a framework 616 F. SHAHSAVARI, R. KOOSHA AND W. YAN to integrate BIM with uncertainty and sensitivity analyses. This framework includes creating an architectural building model in Revit, and retrieving the input parameters for running the required simulations.…”

Section: Introductionmentioning

confidence: 99%

“…A mapping between Revit and Excel is performed through Dynamo, to retrieve the input parameters from the BIM model. Building material properties, the dimensions and Window-to-Wall Ratios (WWRs) are transferred from the Revit model to the Excel-based energy consumption calculation tool, developed by (Shahsavari et al, 2018). Each input parameter is in one of the two statuses of directly available and indirectly available parameters.…”

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confidence: 99%

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Uncertainty and Sensitivity Analysis Using Building Information Modeling - (An Energy Analysis Test Case)

Shahsavari¹,

Koosha²,

Yan³

2019

CAADRIA Proceedings

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Building design decision-making is associated with uncertainties due to variations over time and unpredictable parameters. There is a growing demand for probabilistic methods, i.e., uncertainty and sensitivity analyses to handle the uncertainties in building design. This research intends to encourage the application of Building Information Modeling (BIM) for addressing design uncertainties affecting building energy performance. The mapping between BIM (Revit and Dynamo) and a customized model-based energy analysis tool in Excel is investigated to translate architectural models to energy models and conduct the probabilistic analyses. The application of this method is demonstrated with a test case of a hypothetical residential unit in College Station, Texas, USA. Input variables in this example are the thermal properties of building elements, and the two simulation outputs are annual heating and cooling energy consumption, and deviation from comfort temperature. The results indicate the probability distribution of simulation outputs and the importance factor of each design input. This method deals with uncertainties and provides a more reliable and robust basis for design decision-making.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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