The energy performance of residential buildings depends on a large number of interrelated factors. The present paper outlines an approach to developing a building thermal simulation model through real-time data and sensitivity analyses. To this end, three existing multi-family apartment buildings in Pristina, Kosovo, were selected. Initially, thermal simulation models were created using multiple data sources. Model outputs were further evaluated via comparison with available and measured data. Consequently, the most influential input parameters were identified and adjusted to calibrate the models. The resulting calibrated models can be deployed to investigate the potential of alternative retrofit measures.
Z. Ma , P. Cooper , D. Daly , and L. Ledo , “Existing building retrofits: Methodology and state-of-the-art,” Energ. Build., vol. 55, pp. 889–902, 2012.
P. Hoes , J. Hensen , M. Loomans , B. de Vries , and D. Bourgeois , “User behavior in whole building simulation,” Energ. Build., vol. 41, pp. 295–302, 2009.
A. Mahdavi and F. Tahmasebi , “People in building performance simulation,” in Building Performance Simulation for Design and Oper ation. J. L. M. Hensen and R. Lamberts , Eds., Ch. 4. London: Routledge, pp. 56–83, 2011.
A. Deralla and A. Mahdavi , “Thermal performance analysis of traditional housing in Kosovo,” in Proceedings of the 2nd Central European Symposium on Building Physics, Vienna, Austria, Sep. 9–11, 2013, 2013, pp. 217–221.
Y. Heo , D. Graziano , L. Guzowski , and R. Muehleisen , “Evaluation of calibration efficacy under different levels of uncertainty,” J. Build. Perform. Simulation, vol. 8, no. 3, pp. 135–144, 2015.
Y. Heo , R. Choudhary , and G. A. Augenbroe , “Calibration of building energy models for retrofit analysis under uncertainty,” Energ. Build., vol. 47, pp. 550–560, 2012.
D. N. Zetz and I. Kistelegdi , “Comfort simulation supported sketch plan optimization of the University of Pécs, Medical School extension,” Pollack Period., vol. 15, no. 2, pp. 166–177, 2020.
G. Kővári and I. Kistelegdi , “Building performance simulation modeling techniques,” Pollack Period., vol. 11, no. 2, pp. 135–146, 2016.
G. M. Mauro , M. Hamdy , G. P. Vanoli , N. Bianco , and J. L. M. Hensen , “A new methodology for investigating the cost-optimality of energy retrofitting a building category,” Energ. Build., vol. 107, pp. 456–478, 2015.
P. Paliouras , N. Matzaflaras , R. H. Peuhkuri , and J. Kolarik , “Using measured indoor environment parameters for calibration of building simulation model – A passive house case study,” Energy Procedia, vol. 78, pp. 1227–1232, 2015.
M. Royapoor and T. Roskilly , “Building model calibration using energy and environmental data,” Energ. Build., vol. 94, pp. 109–120, 2015.
M. Rashani and A. Mahdavi , “Calibration of building energy use models: A case study of three residential buildings in Kosovo,” in Proceedings of the 3rd Central European Symposium on Building Physics, Dresden, Germany, Sep. 14–16, 2016, 2016, pp. 521–526.
M. Rashani and A. Mahdavi , “Energy performance assessment of existing multi-family apartment buildings in Kosovo,” Energy Procedia, vol. 78, pp. 782–787, 2015.
SketchUp 8: 3D Design Software, 2014. [Online]. Available: https://www.sketchup.com. Accessed: Jul. 22, 2018.
EnergyPlus, Department of Energy, US, 2015. [Online]. Available: https://energyplus.net/. Accessed: Jul. 22, 2018.
OpenStudio 1.3.0, 2014 , National Renewable Energy Laboratory. [Online]. Available: www.openstudio.net. Accessed: Jan. 10, 2018.
ASHRAE Guideline 14-2002: Measurement of energy and demand savings, American Society of Heating, Refrigerating and Air-Conditioning, Atlanta, GA, 2002.