An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation

• Published in: Energies (Basel) Vol. 12; no. 12; p. 2448
• Main Authors: De Rosa, Mattia, Brennenstuhl, Marcus, Andrade Cabrera, Carlos, Eicker, Ursula, Finn, Donal P.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.06.2019
• Subjects: Accuracy; Alternative energy; Building envelopes; building simulation; Calibration; Complexity; Computer simulation; Cooling; Demand side management; Economic forecasting; Electricity distribution; Energy consumption; Energy demand; Energy efficiency; Energy management systems; energy performance forecasting; External walls; Flexibility; Green buildings; Heat exchangers; Heat pumps; Legislation; Low temperature; model calibration; Nodes; reduced models; Residential buildings; smart grids; Thermal analysis; Canada; Montreal Quebec Canada; Ireland
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12122448

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.