## 3 Results and Discussion
### Case Study with ANN Application  
To prove the practical potential of the created tool, a case study was performed. The intention of this part of the study was to show how the knowledge from the ANN forecast can be applied to optimize energy consumption and decrease the operational costs of the system, as well as minimize the environmental impact by achieving the highest share of renewable energy in the mix. In other words, the purpose of this chapter is to demonstrate the rationale behind the use of the model produced and the predictions it provides in optimizing the resources used. The following describes how using this tool can affect energy efficiency and reductions in external energy demand. Three representative months were selected from available data--December, March, and May. In terms of PV project data, the first month represents the least sunny period of the year, the last month provides the most favorable conditions for PVs, while the middle one represents the intermediate season. The average monthly energy consumption assumed at the projecting stage of the PV installation was 641 kWh, and this value was assumed as a reference. To simplify calculations, the constant consumption per day was selected to be equal to 20.68 kWh. This value served as an unoptimized case, marked as \"Constant\" in Figure 7. The daily consumption in the ANN-optimized case was described using the following equation:  
\[C_{opt}=ANN_{out}*\left(\frac{C_{month}}{Sum_{ANN}}\right) \tag{2}\]  
where ANN\({}_{\text{out}}\) describes the forecasted energy production for the selected day, C\({}_{\text{month}}\) refers to the expected monthly energy consumption (641 kWh), and Sum\({}_{\text{ANN}}\) describes the total sum of all ANN\({}_{\text{our}}\) for the selected month. As a result, the energy consumption is automatically adjusted to match the C\({}_{\text{month}}\) value at the end of the month. However, this strategy can be efficient only for months with a total production similar to or preferably higher than consumption. The visualization for March is presented in Figure 7 (see also Supplementary Materials Table S1). Figure 6: Comparison between ANN and experimental data. As can be observed, the production covered the consumption for almost all days. The gap between these two values was only 11.96 kWh, which means that over 98.13% of the total monthly consumption can be supplied with PV production. Production was not sufficient to fully cover consumption in only 3 out of 31 days. For reference, the same calculation for constant energy consumption indicates only 84.25% of consumption coverage, which results in over 100 kWh that needs to be compensated by purchase from the power grid. The difference between constant and optimized consumption is less pronounced for months with favorable conditions for energy production. In the case study for May, the unoptimized system covered 94.41% of the required energy, while the optimized system reached 98.90%. It is still significant and worth considering the benefits, but for months with high energy production, optimization is less important. For December, where the energy generated is lowest, both approaches give remarkably similar results--36.85% vs. 36.74% for optimized and unoptimized cases, respectively. When the production is low in total, no numeric approach can overcome the physical limits of the system, and in both cases, energy needs to be bought from external sources, leading to negative installation income. Interesting results have been obtained from the economic analysis of both cases. To perform it, several assumptions have been made. First, an average price of 1 kWh was assumed at a level of 0.13C based on current prices in the region of the study. The re-sell value is counted as 80% of the above-mentioned market price. In every case where production exceeded consumption, the profit equal to the sold energy was calculated. Alternatively, if the consumption exceeded production, the loss was measured.