## 3 Methodological Framework
### Data Availability
#### 3.2.2 Data for Energy Demand and Performance  
This analysis examined the kindergarten's energy demand and the performance of its PV system under three scenarios. During the low-usage case, energy demand variations were influenced by changes in occupancy and usage patterns due to pandemic-related restrictions. Austria, including the Southern Burgenland region, experienced multiple lockdowns between March 2020 and December 2021, with schools and public buildings facing closures and limited operations during this period [60]. Detailed time series data, consisting of 8760 hourly final energy consumption measurements for the year 2021, were sourced from the AUORAL platform. These data covered key energy-related components, including the air conditioning (AC) system, heat pump, domestic hot water (DHW) system, PV system, and electrical consumption for lighting and other uses. The analysis specifically relied on the 2021 dataset, as it provided the most complete and representative time series available. This annual dataset served as the foundation for evaluating the building's energy demand and the performance of its systems under hypothetical reduced usage conditions for a period of 25 years. The high-usage period modeled stable operational conditions for 25 years (2024-2049), focusing on the sustainability and economic performance of the PV system. In the absence of operational data for this extended period, the annual electricity demand for the kindergarten was estimated at 10,608.38 kWh, based on regional benchmarks for European schools [61] reporting an average energy use of 120 kWh/m\({}^{2}\)/year. Using the kindergarten's total floor area of 168.85 m\({}^{2}\), this value reflects the projected energy demand under normal operational conditions. To further simulate system interactions, hourly time series data were generated using INTEMA.building, providing a detailed representation of the building's energy demand and interactions among its systems. In addition, as the energy consumption patterns indicated that the PV-based system was overprovisioned considering the actual needs of the building, a third hypothetical scenario was considered, with a PV system that was more rationally sized based on the expected annual electricity demand of the building. This scenario is referred to as \"high-usage (low PV)\" in the rest of the text. To better understand the energy performance under both low- and high-usage conditions, the analysis considered two distinct energy policies: net metering buyback and net billing buyback. The net metering buyback scheme allows the electricity generated and exported to the grid to directly offset the electricity consumed on a 1:1 energy basis. At the end of the billing period, any surplus energy can either roll over as a credit for future use (rolling credit) or be compensated at a predefined rate. This approach generally maximizes economic benefits when energy generation and consumption are balanced over the billing period. The net billing buyback scheme, on the other hand, values the exported energy at a lower rate than the imported energy price. Instead of offsetting energy directly, the policy \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Electrical Demand** & **PV Production** & \multirow{2}{*}{**CAPEX (EUR)**} \\  & **(kWh/year)** & **(kWh/year)** & \\ \hline Low Usage & 7739 & 24,066.99 & 27,158.79 \\ High Usage (Large PV) & 10,608.35 & 24,066.99 & 29,897.83 \\ High Usage (Small PV) & & 13,431.66 & 16,687.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PV Characteristics Across Usage Scenarios. balances the monetary difference between exported and imported electricity. Both policies were examined under various reconciliation periods (yearly, quarterly, monthly, and daily) to analyze their economic impacts. In this context, \"reconciliation\" refers to the timeframe over which the balance between exported and imported electricity is calculated.