Building stock energy model

After the physical properties of the building elements are established in Paper I, the energy demand of the different archetypes can be simulated. For this a monthly steady-state energy balance model based on the Swiss building energy demand calculation standard SIA 380/1 is used [100]. The model has been developed in Paper II and enhanced both in Paper III and Paper IV. Fig. 3.3.1 shows the basic structure of the energy model and the required archetype input variables or constants. The related detailed equations are provided in Paper II.

Fig. 3.3.1 Energy balance and flows of the SwissRes energy model with its respective input variables and constants. (DHW = Domestic Hot Water, HDD = Heating Degree Day, TB =

Methodology & datasets

transmission through the building elements and thermal bridges, together with thermal losses through ventilation of air exchange. The magnitude of these losses depends mainly on the difference between the outdoor temperature and the desired indoor temperature, expressed as cumulated monthly Heating Degree Days (HDD), when the daily average temperature drops below 12°C [100].

Concerning thermal gains, the energy model account for internal gains from occupants and electronic devices as well as for solar gains through the windows.

For the latter, the average monthly irradiation per cardinal direction (north, east, south, west) is derived and aggregated by canton [196]. This is combined with the share of windows by direction from the CECB, to calculate the solar gains per archetype. It should be noted, that only the gains occurring during a heating day are taken into account for the energy balance.

The gap between gains and losses leads then to the required useful space heating energy demand, which can be extended to the demand for DHW by applying default values per archetype from the Swiss building energy standard SIA 380/1 [100]. To arrive at the final energy demand, the useful energy demand is reduced by the efficiency of the heating system. Here it should be noted, that in the case of HPs this implies that the ambient heat source is added to the final energy demand of the supply, but not accounted for the delivered energy demand. This distinction is important when it comes to the evaluation of impacts and cost, as will be discussed later. To avoid a doubling and to better differentiate between the different forms of energy in the text, the “delivered energy” is referred to as

“delivered energy” where applicable. In the last step, the delivered energy can be converted to primary energy or related environmental indicators such as GHG emissions.

The input data for the energy model is derived from different sources and in relation to different aggregation functions as listed in Table 3.3.1. Archetype specific building data is mainly derived from the CECB PLUS dataset [98]. This is supplemented by statistical data of the building stock from the FRDB [193], and normed values from the Swiss building energy Standard [100]. Daily and monthly climate data for different weather stations across Switzerland is provided by the Swiss climate agency [196], which is used to calculate the average temperature and irradiation per canton over the time span of the last five years (2014 to 2019).

Methodology & datasets

Table 3.3.1 Archetype variables and their related sources of the SwissRes model. The table also provides the relationship between each model variable and the various archetype categories, namely AGE, TYPE and Typology, Canton, Element, Element Type or Heating System as well as Retrofit period.

Variable Description Source Aggregation Function of

Building specific data

𝐴 Surface Element [m² / m² ERA] CECB PLUS Median AGE, TYPE, Typology, Element, Element type 𝑎ℎ Installation period of heating system CECB PLUS Median AGE, TYPE, Heating

system

𝑏 b-factor [%] CECB PLUS Constant Element, Element type

𝜂𝑒𝑐 Efficiency of heating system [%] CECB PLUS Median TYPE, Heating system, Retrofit period 𝑓𝑅𝑠 Retrofit share of element surface [%] CECB PLUS AGE, TYPE, Element,

Retrofit period 𝑓𝑤 Share of window orientation by direction

[%] CECB PLUS Mean TYPE, Typology

𝑓_𝑆 Shading of windows by direction [%] CECB PLUS Median TYPE, Typology

𝑔 Solar energy transmittance [%] CECB PLUS TYPE, Element, Retrofit

period

𝑙 Length of thermal bridge [m] CECB PLUS Mean AGE, TYPE, Element 𝜓 Thermal bridges coefficient [W/(m K)] CECB PLUS Mean AGE, TYPE, Element

𝑈 U-value [W/(m² K)] CECB PLUS Median TYPE, Element, Retrofit

period

𝑣̇ Specific ventilation rate [m³ /h /m² ERA] CECB PLUS Median TYPE, Retrofit period Building statistic

𝐷𝑤 Number of dwellings FSO Sum AGE, TYPE, Typology,

Canton, Heating system 𝐷𝑤𝑆 Main dwelling surface (excl. secondary

and holiday residences) [m²] FSO Sum AGE, TYPE, Typology,

Canton, Heating system Climate data

𝐺𝑠

Average monthly cumulated irradiation

[kwh/m²] MeteoSchweiz Mean Canton

𝑛𝐻𝐷 Number of heating days [d] MeteoSchweiz Mean Canton

𝑇 Average daily outside temperatures [ °C] MeteoSchweiz Mean Canton Normed calculation factors

𝜂𝑔 Useful fraction of the building for gains

[%] SIA Sum AGE, TYPE, Typology,

Canton, Heating system 𝑓𝑝𝑒 Conversion factor primary energy SIA Constant Heating system

𝑄_𝑒𝑙 Internal gains from electronic devices

[kWh/m ²] SIA Constant TYPE

The first results of the SwissRes energy model in Paper II, have shown a significant deviation between the modelled energy demand and the measured consumption. It was therefore indispensable to address this EPG to ensure a realistic energy saving potential of retrofit measures. Based on literature, the indoor temperature is assumed to have the largest influence on the EPG [71,152,153]. Therefore, the direct comparison of measured consumption from the

Methodology & datasets

same dataset and the resulting SwissRes energy demand as a function of the indoor temperature variable, allow to establish correction factors for the indoor temperature by construction period and building type, as illustrated in Fig. 3.3.2.

Fig. 3.3.2 Comparison of SwissRes model results for the average final energy space heating demand as a function of the indoor temperature with measured consumption by building type [197]. The diamonds represent the results of the SwissRes model for different internal temperatures, which are used to define the factor for the EPG correction.

The SwissRes model provides all the monthly values for all the different energy flows of Fig. 3.3.1 for all the archetypes from Table 3.2.1, which results in roughly 100,000 rows for each of the 12 output variables (e.g., surface losses, solar gains or primary energy demand). This vast amount of data can then be aggregated to annual values as well as to any combination of archetype categories by weighting all archetypes with their respective share of ERA in the stock.