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Improving energy performance of buildings is one of the most important tasks for reaching sustainability. Assessing building energy consumption is performed more often with specialized simulation tools. Sensitivity analysis proved to be a valuable tool for creating more reliable and realistic building energy models and better buildings. This paper briefly describes the methodology for running global sensitivity analysis and tools that can be used, and presents the results of such an analysis conducted for winter period, daily, on input variables covering a real building's operation, control and occupant related parameters that affect both thermal comfort and heating energy consumption. Two sets of inputs were created. The only difference between these sets is an addition of clothing insulation and occupant heat gain as input variables. The reference building was simulated for three distinctive winter weeks. Two additional input variables have an effect especially on thermal comfort, but they do not disturb the relative order of other influential input variables. The common influential variables for both energy consumption and thermal comfort were identified and are: air handling unit supply temperature and airflow rate and control system related parameters. This can help in future research into implementing the simulation-assisted optimized operation in real buildings.

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IGNJATOVIĆ, Marko G. et al. SENSITIVITY ANALYSIS FOR DAILY BUILDING OPERATION FROM THE ENERGY AND THERMAL COMFORT STANDPOINT. Thermal Science, [S.l.], v. 20, p. S1485-S1500, feb. 2017. ISSN 2334-7163. Available at: <>. Date accessed: 27 june 2017. doi:
Received 2017-02-07
Accepted 2017-02-07
Published 2017-02-07


[1] Perez-Lombard, L., et al., A Review on Buildings Energy Consumption Information, Energy and Buildings, 40 (2008), 3, pp. 394-398
[2] ***, 2010 Buildings Energy Data Book, Office of Energy Efficiency and Renewable Energy, US Department of Energy, 2011,
[3] Gruber, M., et al., Model-Based Controllers for Indoor Climate Control in Office Buildings – Complexity and Performance Evaluation, Energy and Buildings, 68 Part A (2014), Jan., pp. 213-222
[4] Bojić, M., et al., A Simulation Appraisal of Performance of Different HVAC Systems in an Office Building, Energy and Buildings, 43 (2011), 6, pp. 1207-1215
[5] De Wilde, P., The Gap between Predicted and Measured Energy Performance of Buildings: A Framework for Investigation, Automation in Construction, 41 (2014), May, pp. 40-49
[6] Maile, T., et al, A Method to Compare Simulated and Measured Data to Assess Building Energy Performance, Building and Environment, 56 (2012), Oct., pp. 241-251
[7] Raftery, P., et al., Calibrating Whole Building Energy Models: An Evidence-Based Methodology, Energy and Buildings, 43 (2011), 9, pp. 2356-2364
[8] Dong, B., et al., Development and Calibration of an Online Energy Model for Campus Buildings, Energy and Buildings, 76 (2014), June, pp. 316-327
[9] Tian, W., A Review of Sensitivity Analysis Methods in Building Energy Analysis, Renewable and Sustainable Energy Reviews, 20 (2013), Apr., pp. 411-419
[10] Rodriguez, G. C. et al., Uncertainties and Sensitivity Analysis in Building Energy Simulation Using Macroparameters, Energy and Buildings, 67 (2013), Dec., pp. 79-87
[11] Yildiz, Y., Arsan, Z. D., Identification of the Building Parameters that Influence Heating and Cooling Energy Loads for Apartment Buildings in Hot-Humid Climates, Energy, 36 (2011), 7, pp. 4287-4296
[12] Lam, J. C., et al., Sensitivity Analysis and Energy Conservation Measures Implications, Energy Conversion and Management, 49 (2008), 11, pp. 3170-3177
[13] Hopfe, C. J., Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design Optimization, PhD thesis, Eindhoven University of Technology, Eindhoven, Netherlands, 2009
[14] Ioannou, A., Itard, L. C. M., Energy Performance and Comfort in Residential Buildings: Sensitivity for Building Parameters and Occupancy, Energy and Buildings, 92 (2015), Apr., pp. 216-233
[15] Afram, A., Janabi-Sharifi, F., Theory and Applications of HVAC Control Systems: A Review of Model Predictive Control (MPC), Building and Environment, 72 (2014), Feb., pp. 343-355
[16] ****, ASHRAE Handbook – Fundamentals, Chapter 9: Thermal Comfort, American Society of Heating, Ventilation and Air conditioning Engineers, Atlanta (Georgia), USA, 2013
[17] ***, EnergyPlus 8.4, US Department of Energy,
[18] Ignjatović, M., et al., HVAC System Optimization Based on Dynamic Simulation Tool (in Serbian), KGH, 45 (2016), 1, pp. 53-57, in Serbian
[19] ***, SIMLAB. V2.2, Simulation Environment for Uncertainty and Sensitivity Analysis, Developed by the Joint Research Center of the European Commission,
[20] ***, R Development Core Team. R: a language and environment for statistical computing. R foundation for statistical computing,
[21] ***, SPSS Inc. Released 2007. SPSS for Windows, Version 16.0. Chicago, SPSS Inc, com/analytics/us/en/technology/spss/
[22] ***, EnergyPlus EMS Application guide, US Department of Energy,
[23] ***, EnergyPlus Engineering reference, US Department of Energy,

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