## 3 Results
### Global Sensitivity  
#### 3.2.1 Variated Parameters  
Table 2 summarizes the top five ranking parameters (excluding the amount of energy) for each scenario comprising different energy supply systems. Four of the parameters are common for all scenarios: the unit cost of electricity, attic insulation, roof-mounted PV's, and the amount (m\({}^{2}\)) of attic insulation considered in the calculation. Besides the shared parameters, a model-specific parameter is identified for each of the three cases: for DH, it is the unit cost of the new windows; for HP, it is the lifetime of the ground source HP system; and for gas, it is the unit cost of the fuel. As discussed in the previous section, the electricity price is a rather complex parameter to predict. Two of its determining factors are the annual amount of supplied electricity to the client and the type of heating system (e.g., electrical or another source for space building heating). In that sense, the \"profile\" of the building owner may change when switching from a fuel-based to an electrical heating system. This is firstly due to the larger amount of purchased electricity (usage interval); secondly, it is due to additional fees if the building is heated by electrical energy [38]. The unit cost difference in the electricity price can be observed in Figure 6, which shows historical and forecasted data for two distinct usage intervals for electricity and gas. The figure collects data from the DEA price database [33] and another DEA publication on \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Rank according to Sensitivity Index**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{District heating} & unit cost & unit cost & unit cost & amount & unit cost \\  & electricity & attic insulation & roof PV & attic insulation & new windows \\ \multirow{2}{*}{Heat pump} & lifetime & unit cost & unit cost & amount & unit cost \\  & heat pump & electricity & attic insulation & attic insulation & roof PV \\ \multirow{2}{*}{Gas} & unit cost & unit cost & amount & unit cost \\  & natural gas & electricity & attic insulation & attic insulation & roof PV \\ \hline \hline \end{tabular}
\end{table} 
Table 2: First five most sensitive parameters for each system, excluding the energy required for building operation. Background color identifies identical input parameters for each energy supply system. socio-economic calculations, including the cost of energy [39]. The latter provides forecast values for electricity and gas costs for the period from 2020-2040. The forecast is based on 2019 values, where the electricity cost was disclosed for a single usage interval, while for gas, a forecast for several different intervals was available. The solid bars in Figure 6 represent electricity prices for the second largest interval (<15 MWh), while the patterned addition depicts the unit cost for the smallest usage interval (used to determine unit costs in the baseline model). The electricity demand in the LCC model is represented by a single input value accounting for the total building demand (operation and private). The majority of the electricity demand in the DH and gas scenarios is attributed to private apartments, in contrast to the HP scenario where electricity for building operation accounts for approximately 80% of the total demand [28; 29]. Thereby, for the DH and gas scenarios, the majority of electricity would be \"paid\" at the highest price (a low usage interval), whereas for the HP scenario, an increased electricity demand (101 MWh/y) classifies the building owner in the highest usage interval (>15 MWh). For 2018, the difference between the smallest and largest interval is 60 EUR/MWh el (approximately 60%). This difference is not taken into account for the HP scenarios in the analysis presented in [28]. From Figure 5 and Table 2, it is evident that the considered electricity price significantly impacts the output.