Application of a Monte Carlo model to predict space heating energy use of Belgrade's housing stock

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 model's 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 model's prediction.

Keywords:

• Monte Carlo method
• uncertainty analysis
• explorative scenarios
• domestic space heating energy use
• domestic energy models

Notes

1. District heating in Belgrade provides no means for occupants to directly regulate the heat supplied by the radiators located in dwelling rooms, such as through the use of thermostats. Instead the thermal plants regulate the supply water temperature either manually or automatically in relation to the external temperature (Energoprojekt Entel 2006).

2. CV is a normalized measure of the dispersion of data points in a data series around the mean.