## 3 Results
### Local Sensitivity--One-at-a-Time (OAT) Approach
#### 3.1.2 Model Inputs  
The second investigation employing OAT methodology was applied to building-related model inputs, defined in Table 1. As described in Section 2.2, the OAT calculations of the model inputs are used to identify the most impactful parameters for the output--in this case, the NPV. Figure 5 shows the sensitivity index of each parameter and each investigated energy supply system. The sensitivity indexes depicted in Figure 5 are calculated following Equation (2) for the respective ranges stated in Table 1.  
For the three investigated systems, the parameters related to the fuel cost and amount rank highest. An exception is present in the Heat Pump (HP) scenario, where the lifetime of the HP outranks the electricity amount with the second-highest sensitivity index. A plausible explanation for this is the high investment (and thereby replacement) cost of a HP. Applying the upper and lower limit values for the HP's lifetime in the OAT variation results in one extra or less system replacement for the calculation period. The remaining parameters that follow the fuel cost seem to be ranked (positioned), if not in the same order, then very close to the same order--all the way to the least sensitive inputs. Figure 4: Sensitivity index for three discount rates (DR) as a function of calculation period. Figure 3: Sensitivity index for different discount rate (DR) types and price developments (PD) compared to the baseline (decreasing DR and no applied PD), considering 50-year calculation period. Despite their front-ranking position, the parameters describing the amount of electricity, gas, and district heating energy were excluded from variation in the global analysis. This decision was made because the sensitivity of energy demand is a topic of its own that is highly dependent on the method for deriving the final value of the delivered energy. The results in Figure 5 show that the amount of energy has a significant influence, regardless of the applied system. Therefore, one should be sure of the value's origin (or used method) when striving for a high-accuracy LCC. Figure 5: Results from variating input parameters using OAT approach. Capital letters represent the input category: O—operation, I—implementation, M—maintenance cost, L—lifetime (years), and A—amount (kWh, m\({}^{2}\), m\({}^{3}\), etc.). In contrast, the unit cost of the different fuels is considered in the global sensitivity analysis. Energy unit costs are also a rather complex parameter. The main uncertainty in the development of the fuel cost lies in the stability of the global energy market as well as national and local factors (subscription fees, local rates, and building usage type). The energy unit cost can be quantified by historical and/or forecasted data. In this study, the price variation for electricity and gas is determined based on historical and forecasted data, stemming from supply plants operating with various sources and different principles. As evident from the DEA database [33], those are also characterized by different costs. While the unit cost varies on a national level, the value for a specific building depends on the location. Therefore, the DH unit price is considered deterministic since the owner would have to pay the cost of DH for the region where the building is located. Regarding the results shown in Figure 5, the DH unit cost presents the single largest sensitivity index. Due to its deterministic essence, the DH unit cost is not varied with the MC method; however, it is still accounted for in the analysis. This is performed by accounting for the large national price variation by fixing the DH unit cost at a low, medium, and high value and variating the next five most impactful parameters. The low and high DH unit costs are chosen as 2\(\sigma\) from the mean value (low = 34, medium = 68, and high = 108 EUR/MWh). In essence, three variations incorporating the aforementioned DH prices are calculated for the DH scenario.