# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
## 5 Discussion  
When assessing a medium-to-high-power PV plant, the initial investment, projected efficiency, and parameters of the desired plant are correlated, and sometimes, a cheaper and less efficient power plant can be recommended. For a more comprehensive analysis, the paper presents two methods, ANOVA and STEM, which are derived from quality control. Regarding statistical methods, the PVGIS platform, which contains input and output data, was presented to predict electricity generation for a PV power plant based on geographical and meteorological parameters, as well as the technical data of the panels used. Based on the data provided by the PVGIS platform, an analysis is conducted to examine the impact of panel tilt angle on electricity production. Cases A and B prove that using a fixed-tilt panel for generating maximum annual energy amount (as provided through PVGIS) is about 37\({}^{\circ}\), but this is not the best solution because there are substantial variations in the monthly production. By looking for the criterion of three consecutive months, for the case of latitude 45\({}^{\circ}\), an optimal seasonal tilt angle would be the following:  
* 30\({}^{\circ}\) for June, July, and August;
* 50\({}^{\circ}\) for September, October, and November;
* 70\({}^{\circ}\) for December, January, and February;
* 40\({}^{\circ}\) for March, April, and May. From Table 3, the annual energy production for the optimum tilt angle of 37\({}^{\circ}\) is 1327 MWh, while the sum of energy with seasonal tilt angle is 1354 MWh. Seasonal activities to modify the tilt angle are not economically justified unless they can be incorporated into periodic maintenance procedures. Solar tracking systems, such as single-axis or dual-axis trackers, can increase energy production by 28% or 31% compared to fixed panels. However, they are more complex, require maintenance, and may not be cost-effective for all applications. Next, the economic efficiency of five different PV power plants was analysed using the STEM statistical method, based on the data presented in the bibliography. The technical and economic parameters considered in this analysis are the declared efficiency of the photovoltaic panels, the power and panel surface according to the catalogue data, the specific cost of a panel (EUR/WP), the estimated losses, and the presence or absence of a tracking system. The data analysis showed that the most efficient are those with panelswith monocrystalline Si technology without a tracking system. Although from a technical point of view, the PV power plant with HCPV technology (utilising a concentrator and tracking system) is the most efficient, from an economic perspective, it is surpassed by the other four systems. Thus, as a perspective for applying the STEM statistical method, an additional parameter should be introduced regarding the effective cost of the land rented or purchased for a PV power plant to highlight the adequate savings obtained by applying HCPV technology. Monocrystalline silicon panels have been considered the most economically efficient solution, especially for medium-to-small-scale projects, due to their high efficiency, long lifespan (25-30 years), and high performance per square meter. Although tracking systems can enhance energy output, various studies have identified key factors that may limit their overall financial viability. These include the significantly higher initial investment costs (estimated to increase total system expenditures by 25-40% due to the addition of motors, gears, sensors, and control software), as well as increased risk of mechanical failure, particularly in environments with ice, sand, or heavy precipitation that can impair tracking functionality. Furthermore, the system's energy consumption, while relatively low, also contributes to operational costs. In addition, tracking the sun is minimally beneficial in high-latitude regions or areas with frequent cloud cover.