Periodicidad de pago de intereses

Each year of the projection period for every developed scenario has been modelled within the MC model and the cumulative change in the total dwelling area resulting from newly-built and demolished dwellings is incorporated into the model. Considering the uncertainties around the space heating consumption of both the renovated dwellings and those that will be built by 2030, the distributions have been ascribed to the dwelling energy classes, efficiency of gas boilers, and the DH system efficiency (see Table 3.5). Triangular distribution has been used to define dwelling energy class and gas boiler energy band, and the lognormal distribution has been used to describe the DH system efficiency as in the base case year (see Subsection 3.4.4). Triangular distribution is useful in instances where little or no data is available (Smith, 2009) and for modelling design data as the most likely value and limits can be grouped, but it over-emphasises extreme values and as such draws attention to them (even though not as much as the uniform distribution) (Macdonald, 2002).

The distribution intervals for dwelling energy classes and gas boiler efficiencies are defined based on the requirements prescribed within the ‘Regulation on Thermal Efficiency of

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Buildings’ (Sluzbeni glasnik, 2011) and by the Seasonal Efficiency of Domestic Boilers in the

UK (SEDEBUK). The same standard deviation of DH system efficiency as in the base case year has been maintained over the projection period and the mean values are defined in accordance with the assumptions developed by the author (see Subsection 3.5.1). The results of uncertainty analysis related to the four explorative scenarios are given in Chapter 7.

Table 3.5 Dwelling energy classes, gas boiler efficiency bands, and DH system efficiency

with distributions, most likely/mean values, and interval/SD

Energy class Distribution Most likely/Mean Interval /SD

Dwelling energy class (kWh/m²a)

A Triangular 15 10, 17

B (new/existing) Triangular 30/35 21, 33/18, 38

C (new/existing) Triangular 50/60 34, 65/39, 75

D Triangular 95 75, 113

For one energy class Triangular 110 80, 150

Gas boiler energy band (%)

A Triangular 90 90, 95 ≥B Triangular 86 86, 90 ≥C Triangular 82 82, 86 ≥80 Triangular 80 80, 84 DH system efficiency (%)* 2010 Lognormal 0.750 0.016 2015 Lognormal 0.780 0.016 2020 Lognormal 0.800 0.016 2025 Lognormal 0.830 0.016 2030 Lognormal 0.850 0.016

Note: * 5% trimmed mean.

3.6 Summary

This chapter presents the methodology employed by the Belgrade domestic energy and carbon model (BEDEM). The model is based on both the external data sources and data collected on- site. The obtained information has enabled the following actions: first, to identify the major thermal and construction characteristics of Belgrade’s residential stock which have then been used to stratify the housing stock for the monitoring survey; second, to define the dwelling archetypes which together can represent all dwellings within the stock; and third, to formulate the explorative scenarios for reduction of the domestic space heating energy consumption and the associated carbon dioxide emissions.

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Current trends in building fabric and end-use efficiency have been estimated within the ‘Base Model’ scenario which describes a future where the existing Building Regulation standards and refurbishment activities remain unchanged by 2030, and against which the explorative scenarios have been benchmarked. The delivered space heating energy consumption of each dwelling archetype has been determined by the whole building dynamic energy simulation software ‘TRNSYS’. Whilst the average household energy use of lights and appliances, and cooking, has been estimated using the empirical data on energy use of various lights and appliances in conjunction with the results of the questionnaire survey and information on the ownership of lights, appliances and kitchen ranges, the average household domestic water consumption has been calculated using the steady-state physical equation. Validation of the BEDEM model has been done by comparing the total energy consumption of Belgrade’s housing stock predicted by the model with the official top-down data, and by comparing the space heating energy predictions attributable to the building archetypes with district heating to the measured data.

Four explorative scenarios for reducing the residential space heating energy consumption and associated carbon emissions have been developed, namely: the ‘Demand 1’, the ‘Demand 2’, the ‘Supply’, and the ‘Demand 2 and Supply’ scenarios. The ‘Demand 1’ scenario considers building fabric refurbishment of around 40% of the housing stock to the dwelling energy class ‘D’ (≤113kWh/m2

a) and the ‘Demand 2’ scenario includes refurbishment of half as much to the dwelling energy class ‘C’ (≤75kWh/m2

a). By contrast, the ‘Supply’ scenario considers only improvement of the DH system efficiency for around 13% by 2030. Finally, the ‘Demand 2 and Supply’ scenario integrates the measures considered within the ‘Demand 2’ and the ‘Supply’ scenario. The generic optimisation program ‘GenOpt’ has been then used to obtain the best thermal performance of SFH and MSB 1946/70 by performing the optimisation of four designed input parameters, namely: external wall insulation, roof insulation, U-window, and indoor temperature. The search for the minimum of the objective function has been constrained by both the maximum allowed energy consumption for particular dwelling energy class and the PPD for the ‘B’ class (≤10%) of thermal comfort.

The local sensitivity analysis has been used to identify the most important input parameters from a large set of influential parameters. Furthermore, the principles of linearity have been tested on the five input parameters with the highest sensitivities and the insulation thickness, whilst the principles of the superposition have been investigated on the normalised sensitivity coefficients of the input parameters which show an approximately linear effect on changes in the output variable. Lastly, the MC model has been developed in order to explore and quantify the overall uncertainty in both the BEDEM model predictions in the base case year and scenario assumptions on the projected energy and carbon reduction.

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