An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation

Mattia De Rosa, Marcus Brennenstuhl, Carlos Andrade Cabrera, Ursula Eicker and Donal P. Finn
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Mattia De Rosa: School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland
Marcus Brennenstuhl: Centre for Sustainable Energy Technology, University of Applied Science Stuttgart, 70174 Stuttgart, Germany
Carlos Andrade Cabrera: School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland
Ursula Eicker: Centre for Sustainable Energy Technology, University of Applied Science Stuttgart, 70174 Stuttgart, Germany
Donal P. Finn: School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland

Energies, 2019, vol. 12, issue 12, 1-20

Abstract: The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building.

Keywords: building simulation; model calibration; reduced models; smart grids; energy performance forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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