TY  - JOUR
AU  - Kavgic, Miroslava
AU  - Summerfield, Alex
AU  - Mumovic, Dejan
AU  - Stevanović, Žarko M.
PY  - 2015
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/834
AB  - Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock models predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is +/- 19% the mean value, 50% of them were within +/- 8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and - where possible - quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated models prediction.
PB  - Taylor & Francis
T2  - Journal of Building Performance Simulation
T1  - Application of a Monte Carlo model to predict space heating energy use of Belgrades housing stock
VL  - 8
IS  - 6
SP  - 375
EP  - 390
DO  - 10.1080/19401493.2014.961031
ER  - 

@article{
author = "Kavgic, Miroslava and Summerfield, Alex and Mumovic, Dejan and Stevanović, Žarko M.",
year = "2015",
abstract = "Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock models predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is +/- 19% the mean value, 50% of them were within +/- 8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and - where possible - quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated models prediction.",
publisher = "Taylor & Francis",
journal = "Journal of Building Performance Simulation",
title = "Application of a Monte Carlo model to predict space heating energy use of Belgrades housing stock",
volume = "8",
number = "6",
pages = "375-390",
doi = "10.1080/19401493.2014.961031"
}

Kavgic, M., Summerfield, A., Mumovic, D.,& Stevanović, Ž. M.. (2015). Application of a Monte Carlo model to predict space heating energy use of Belgrades housing stock. in Journal of Building Performance Simulation
Taylor & Francis., 8(6), 375-390.
https://doi.org/10.1080/19401493.2014.961031

Kavgic M, Summerfield A, Mumovic D, Stevanović ŽM. Application of a Monte Carlo model to predict space heating energy use of Belgrades housing stock. in Journal of Building Performance Simulation. 2015;8(6):375-390.
doi:10.1080/19401493.2014.961031 .

Kavgic, Miroslava, Summerfield, Alex, Mumovic, Dejan, Stevanović, Žarko M., "Application of a Monte Carlo model to predict space heating energy use of Belgrades housing stock" in Journal of Building Performance Simulation, 8, no. 6 (2015):375-390,
https://doi.org/10.1080/19401493.2014.961031 . .