Cameroon is a country with great potential for photovoltaic solar energy [1] . In the country’s northern regions, many areas are isolated from the main electricity grids [2] . As a result, in some rural areas, thermal power plants or other generators are distributed in isolated, decentralized forms. The study of the performance of solar panels used in this rural area provides an overall assessment of the lifetime not only of the solar panels, but also of the entire installation, in order to avoid the expense of implementing this photovoltaic solar system. In fact, there are a number of photovoltaic generators in several localities that have been abandoned, partly because the installation was incorrectly sized, and partly because maintenance is not carried out regularly [3] . What’s more, periodic maintenance is not carried out. This neglect automatically leads to many solar power plants coming to a standstill [4] . On the other hand, the problem lies in the use or choice of appropriate components for the installation of solar panels. In this work, a choice is made on the use of photovoltaic cells according to the availability and performance of the chosen site [5] . The University of Ngaoundéré was chosen as the site for an experimental study of solar panels. The results of the experimental study in [6] show that polycrystalline cells are better suited to the Niger locality and can therefore deliver higher yields than polycrystalline cells. In [7] , a study shows why most solar fields in rural areas are abandoned when the lifetime of these installations exceeds four years [8] . However, the work in [9] shows that monocrystalline cells are suitable for low-intensity insolation. This is because polycrystalline cells tend to improve yields when the lifetime or operating time is long. On the other hand, in [10] , it was shown that polycrystalline cells performed better for daily, weekly and monthly periods. These studies have paved the way for the appropriate choice of photovoltaic installations in the city of Ngaoundéré and attracted producers of the materials.

The focus is on climatic conditions on the one hand, and on performance criteria due to the effects of aerosols on the other [11] . Most often, solar panels are installed haphazardly in rural localities, because no feasibility study has been carried out, either due to a lack of appropriate equipment, or because the local population lacks the appropriate equipment or tools to install these photovoltaic systems [12] . The weekly, monthly and annual assessment of sunshine levels in the city of Ngaoundéré gives us an idea not only of the lifespan of the installation, but also of the choice of equipment in terms of performance for the installation of a large-scale photovoltaic power plant.

The first step is to estimate the daily, monthly and annual data before obtaining the annual average. The study was carried out over four successive years, with the most acceptable levels of sunshine observed during the months of December, January, February, March, and April.

Knowing the availability of sunlight is important for estimating the performance of a photovoltaic cell. Not only does this help to determine the size of the photovoltaic power plant that can be set up in that locality, but it also enables a decision to be made on whether to combine other sources of energy, itself renewable, in order to raise the power level, or on the choice of the appropriate cell type. It is, therefore, useful to know the characteristics [13] , performance and efficiency of this experimental study, as shown in Figure 1 .

The various photovoltaic energy conversion stages are shown in Figure 2 . Depending on the type of photovoltaic cell, energy is delivered to non-linear loads via two conversion stages: Direct Current to Direct Current (DC/DC) conversion and Direct Current to Alternative Current (DC/AC) conversion [14] . A Maximum Power Point Tracking (MPPT) control system ensures maximum extraction of the photo power generated [15] . A battery bank is associated with the photovoltaic (PV) system to store continuous energy. This configuration system takes 2 types of solar cells as input: monocrystalline and polycrystalline [16] .

The data acquisition bench installed on the roof of the University of Ngaoundéré enables real-time meteorological data to be extracted during specific intervals of the day. Depending on the station frequency setting, data is generated every 30 minutes or every hour. These data, which are supplied by the University of Ngaoundéré’s data acquisition system, enable a comparative study to be made of the data supplied by the NASA site [17] . Figure 3 shows the location of the real-time meteorological data acquisition site. This site is located in the Adamaoua region of Cameroon. The Cameroonian region of Ngaoundéré is situated in the sub-Sahelian climate zone [18] . Our study was conducted on the University of Ngaoundéré's website. The data were collected and made available by the Institute of University Technology’s (IUT) meteorological station from 01 December 2021 to 01 December 2024.

In order to carry out this study, data will be collected from December 1, 2021 to December 1, 2024. Table 1 shows the geographical coordinates of the chosen location [19] .

The different stages of electrical energy conversion are given in Figure 2 , which represents the architecture of the system on which our work focuses. We observe that it is important to add a boost chopper and to extract energy in a battery bank. The sensors are installed to collect real-time data or for the acquisition of experimental data.

The power generated from the photovoltaic panel is defined by the cell temperature as a function of the location’s ambient temperature and the available solar irradiation [20] . The PV power is used to supply the load directly in case of outages, and/or to charge the battery bank. The maximum available power from the PV with maximum power point tracking (MPPT) for a given surface area of the panel, A_pv (m²), and the total irradiation hitting the PV surface, G.

The photovoltaic system is a combination of solar panels, a DC converter and a DC-AC converter stage to supply either direct DC loads or linear or non-light AC loads [21] . Figure 4 shows the electrical system configuration of the stage for extracting photovoltaic energy from sunlight. This diagram depicted in Figure 4 allows us to develop a mathematical model of the power of a photovoltaic generator using the photocurrent generated, according to Equation (1) [22] .

$I_{\text{out}}=I_{\text{In}}-I_0\left\{\exp \left[\frac{\text{e}}{{\text{kT}}_C}\left(V_{\text{out}}+R_y{I}_{\text{out}}\right)\right]-1\right\}-\frac{V_{\text{out}}+R_x{I}_{\text{out}}}{R_x}$ (1)

$V_{\text{out}}=\frac{{\text{AkT}}_c}{\text{e}}\ln \left(\frac{I_{\text{In}}+I_o-I_{\text{out}}}{I_o}\right)-R_y{I}_{\text{out}}$ (2)

$P_{\text{Syst}}\left(\eta \right)={\eta }_{{}_{\text{Syst}}}{A}_{\text{Syst}}{G}_{\alpha }\left(\eta \right)$ (3)

where $P_{\text{Syst}}\left(\eta \right)$ is the generated PV power at time step t (a 1-min time step is used) and ${\eta }_{{}_{\text{Syst}}}$ is the PV panel efficiency and is given by.

${\eta }_{{}_{\text{Syst}}}={\varphi }_{\gamma }\left[1-{\beta }_{\rho }\left(T_{\text{cell}}-T_{\text{STC}}\right)\right]$ (4)

$T_{\text{cell}}=T_{\text{amb}}+\left(\text{NOCT}-20\right)\frac{G_{\varphi }}{800}$ (5)

where $G_{\varphi }$ is the module efficiency, ${\beta }_{\rho }$ is the photovoltaic panel power temperature coefficient taken as −0.003/˚C for silicon cells. T_cell and T_amb are the cell and ambient temperatures (˚C), and T_STC is the reference temperature, which is 25˚C. The nominal operating cell temperature (NOCT), module efficiency, and temperature coefficient are variables depending on the selected module and can be derived from the manufacturers’ datasheets.

Mathematical techniques such as statistical tests can be used to evaluate models [23] . These include the Root Mean Square Error, the Mean Bias Error, the Mean Absolute Error, the Mean Absolute Relative Error, or the quadratic error, which allow us to evaluate the difference between two data sets, one measured and the other estimated or theoretical. The data collected by the acquisition bench at the University of Ngaoundéré are compared with the data provided by the NASA site, in order to determine the quadratic error.

The results section deals with the power profiles of different photovoltaic solar cells: monocrystalline cells and polycrystalline cells. The evaluation of the power profiles is first made for one day, and then a comparative study of the data provided over four consecutive months is also carried out, namely, January, February, March and December. An annual average is calculated to determine the power level of the entire energy system.

Table 2. Data collected for monocrystalline silicon solar cells (MSSC).

Determining the values of the performance indicators listed in Table 2 gives an idea of the estimation or extrapolation of real-time data. Table 3 shows the various parameters determined by the use of the monocrystalline cell. We can see that for the first four successive years, there is a regression or decrease in cell efficiency performance, but this decrease is not too great.

The values in Table 3 , on the other hand, show that there is a big difference and a gradual decrease in polycrystalline cell efficiency. This shows that when the time of use of photovoltaic generators extends over a long period for polycrystalline materials, it is possible that there is a decrease in solar cell performance [29] . The values of these performance indicators show that not only is the approach or method better for power extrapolation, but it also gives an idea of the feasibility and implementation of a high-capacity photovoltaic generator.

It can be seen that for four successive years, there has been a decrease in the power produced by polycrystalline cells. This regression means that polycrystalline cells are either unsuitable for the chosen site, or do not perform better in these climatic conditions.

After evaluation over a whole day, we can see that for a sunny day, particularly in February and March-April, there is a drop in performance for generators made from polycrystalline cells, whereas for solar panels made from monocrystalline cells, there is a slight increase in performance is observed, up to 18% in efficiency. These results corroborate studies in the literature which show that monocrystalline solar panels are more resistant and suitable for rural areas with low levels of sunlight. However, not only is the local sunshine low, but the environment is also exposed to dust. However, the performance of monocrystalline cells is quite acceptable, demonstrating the robustness of these materials in the face of adverse weather conditions. The aim is not only to evaluate the performance of monocrystalline cells, but also to assess the performance parameters of amorphous cells for the same locality, in order to gain an idea of the choice of materials for the installation of photovoltaic systems.