Abstract
Building energy models are increasingly used in energy renovation projects to identify the best retrofit strategy. However, a significant discrepancy between real and numerical building performances (“performance gap”) is generally observed, which can lead to an erroneous design of the energy retrofit measures. To reduce this gap, automatic model calibrations can be undertaken. This approach generally focuses on fine-tuning some “fixed” parameters to minimize an error function but often disregards the uncertainties in time-varying occupants’ behavior patterns. These latter are also commonly modeled through standardized profiles due to a lack of knowledge, then further increasing the performance gap, especially where occupants’ behavior may have a higher level of uncertainty, as in residential buildings. In this context, it is important to understand and quantify the impact of actual occupancy profiles on model accuracy also in comparison with that achieved through calibration. For this reason, this work compares, for a specific case study of social housing in Reggio Emilia (Italy), the performance gap reduction achievable through (i) common automatic calibration approaches; (ii) the modeling of the actual, experimentally observed, occupants’ behavior. The results reveal that modeling the actual users’ behavior decreases the error (RMSE) in indoor air temperature by 0.46 °C, i.e., more than the reduction obtained through the adopted calibration approaches (0.26 °C). In terms of energy consumption for space cooling, the performance gap without actual occupancy was significantly higher than that obtained for three monitored unoccupied apartments (AC always on), i.e., 10–15% against 1–4%. However, if the actual occupants’ behavior is modeled, the performance gap is reduced to the values obtained for the unoccupied apartments. This study highlights the importance of occupancy patterns in building energy modeling.
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References
Baldoni, E., Coderoni, S., Di Giuseppe, E., et al.: A software tool for a stochastic life cycle assessment and costing of buildings’ energy efficiency measures. Sustainability 13, 7975 (2021). https://doi.org/10.3390/su13147975
Di Giuseppe, E., D’Orazio, M., Du, G., et al.: A stochastic approach to LCA of internal insulation solutions for historic buildings. Sustainability 12, 1535 (2020). https://doi.org/10.3390/su12041535
Maracchini, G., Di Filippo, R., Albatici, R., et al.: Sustainable retrofit of existing buildings: impact assessment of residual fluorocarbons through uncertainty and sensitivity analyses. Energies (Basel) 16, 3276 (2023). https://doi.org/10.3390/en16073276
De Wilde, P.: The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom. Constr. 41, 40–49 (2014). https://doi.org/10.1016/j.autcon.2014.02.009
Yoshino, H., Hong, T., Nord, N.: IEA EBC annex 53: total energy use in buildings—analysis and evaluation methods. Energy Build 152, 124–136 (2017). https://doi.org/10.1016/j.enbuild.2017.07.038
Chong, A., Augenbroe, G., Yan, D.: Occupancy data at different spatial resolutions: building energy performance and model calibration. Appl. Energy 286, 116492 (2021). https://doi.org/10.1016/j.apenergy.2021.116492
LIFE19 CCA/IT/001194 Project.: SUPERHERO—SUstainability and PERformances for HEROTILE-based energy efficient roofs. https://www.lifesuperhero.eu/. Accessed 24 March 2023
Maracchini, G., et al.: A set of calibrated BEMs for real demonstration cases and proposed standardisation. H2020 BIMSPEED Deliverable D3.4 (2023)
Chong, A., Gu, Y., Jia, H.: Calibrating building energy simulation models: a review of the basics to guide future work. Energy Build 253, 111533 (2021). https://doi.org/10.1016/j.enbuild.2021.111533
Maracchini, G., D’Orazio, M.: Improving the livability of lightweight emergency architectures: a numerical investigation on a novel reinforced-EPS based construction system. Build. Environ. 208, 108601 (2022). https://doi.org/10.1016/j.buildenv.2021.108601
Maracchini, G., Di Giuseppe, E., D’Orazio, M.: Impact of occupants behavior uncertainty on building energy consumption through the Karhunen-Loève expansion technique: a case study in Italy. Smart Innov., Syst. Technol. 263, 197–207 (2022). https://doi.org/10.1007/978-981-16-6269-0_17
Li, J., Yu, Z. (Jerry), Haghighat, F., Zhang, G.: Development and improvement of occupant behavior models towards realistic building performance simulation: a review. Sustain. Cities Soc. 50, 101685 (2019). https://doi.org/10.1016/j.scs.2019.101685
Maracchini, G., Di Giuseppe, E., D’Orazio, M.: Energy poverty and heatwaves. Experimental Investigation on Low-Income Households Energy Behavior pp 271–280 (2023)
Martín-Garín, A., Millán-García, J.A., Baïri, A., et al.: Environmental monitoring system based on an open source platform and the internet of things for a building energy retrofit. Autom. Constr. 87, 201–214 (2018). https://doi.org/10.1016/j.autcon.2017.12.017
Calama-González, C.M., Symonds, P., Petrou, G., et al.: Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring. Appl. Energy 282 (2021). https://doi.org/10.1016/j.apenergy.2020.116118
DOE.: Energyplus Version 9.4 Documentation: Engineering Reference (2021)
Tindale, A.: DesignBuilder Software. Design-Builder Software Ltd (2005)
ISO—International Organization for Standardization.: UNI/TS 11300-1 energy performance of buildings—part 1: evaluation of energy need for space heating and cooling (2014)
ISO—International Organization for Standardization.: UNI/TS 11300-2 energy performance of buildings—part 2: evaluation of primary energy need and of system efficiencies for space heating, domestic hot water production, ventilation and lighting for non-residential buildings (2014)
CEN—European Committe for Standardization.: EN 16789-1 energy performance of buildings. Ventilation for buildings—indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Module M (2019)
ISO—International Organization for Standardization.: UNI EN 15193-1 energy performance of buildings—energy requirements for lighting—part 1: specifications, module M9 (2021)
ASHRAE Stand.Comm.: Guideline 14 measurement of energy and demand savings (2014)
Acknowledgements
This work was supported by the Project LIFE SUPERHERO (LIFE19 CCA/IT/001194) “Sustainability and PERrformaces for HEROTILE-based energy efficient roofs”, performed with the contribution of the European Union’s LIFE Programme “Climate Change Adaptation”.
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Maracchini, G., Latini, A., Di Giuseppe, E., Gianangeli, A., D’Orazio, M. (2024). What Matters the Most? The Role of Actual Occupancy Patterns and Automatic Model Calibration in Reducing the Building Energy Performance Gap in an Italian Case Study. In: Littlewood, J.R., Jain, L., Howlett, R.J. (eds) Sustainability in Energy and Buildings 2023. Smart Innovation, Systems and Technologies, vol 378. Springer, Singapore. https://doi.org/10.1007/978-981-99-8501-2_22
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