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BY 4.0 license Open Access Published by De Gruyter Open Access August 17, 2022

Efficiency analysis of photovoltaic systems installed in different geographical locations

  • Muhammet Demirkiran EMAIL logo and Abdulhakim Karakaya
From the journal Open Chemistry


Many forms of energy are used in daily life. The most needed of these different forms is electricity. If this energy continues to be met with limited resources, it is clearly seen that sufficient energy needs will not be met in the future. Therefore, in the generation of electrical energy, existing resources must be used extremely efficiently. With the development of technology, electrical energy production is carried out more efficiently with non-renewable energy sources. These technological developments, which come to a certain point, can meet the demanded energy need up to a certain level. For this reason, many studies are carried out on renewable energy sources in order to respond to the needed energy demands. Therefore, in this study, the effect of geographical conditions on panel efficiency in electricity generation with solar panels, which is one of the renewable energy sources, was analyzed. Analyzes were carried out with power plant models installed in four different geographical regions of Turkey using the design and simulation software for photovoltaic systems program for PV systems. The effects of latitude, altitude and temperature on electrical energy production were investigated using the established power plant models.

1 Introduction

Photovoltaic (PV) panels work according to the principle of generating electric current by the release of electrons in semiconductor cells by the sun’s rays. The sun is a source of both heat and light. PV panels, on the other hand, need light. However, solar heat negatively affects PV panels. PV systems are exposed to different light and heat depending on geographical regions, latitude and longitude. Therefore, the geographic location where the PV systems will be installed will affect the panel efficiency.

When the literature studies on the efficiency analyses of PV systems depending on changing conditions are examined, Buni et al. examined the effect of solar radiation increase on panel efficiency [1]. Adak et al. examined the changes in the output power of PV panels depending on temperature and solar radiation [2]. Usman et al. investigated the performance of PV systems depending on solar radiation, shading, dust and geographical location [3]. Rodziewicz et al. examined the efficiency analysis of PV systems on sunny and cloudy days [4]. Koo et al. performed the efficiency analysis of solar radiation, which changes due to climatic and environmental conditions, with the monthly average daily solar radiation model, which makes a highly accurate prediction using an artificial neural network [5]. Demir and Özkan investigated the efficiency of temperature and zenith angle on PV panels in their study in Konya, Turkey [6]. Bhol et al. investigated the effects of different environmental factors such as dust, color, radiation and shading on the performance of solar panels [7]. Dubey et al. studied the operating temperature of solar cells and their effects on PV systems [8]. Ye et al. examined the roofing material, ventilation, panel framing and other environmental conditions that affect the panel temperature of PV systems installed in the tropics [9]. Narkwatchara et al. investigated the effects of 2.5 micron particulate matter, ambient temperature and other factors on PV systems in tropical climates [10]. Bergin et al. studied the varying solar radiation of atmospheric particles in different geographies and the efficiency of PV systems in places with high levels of dust or particulate matter [11]. Al-Bashir et al. investigated the wind speed, solar radiation intensity and cell temperature difference affecting the PV system established at Hashemi University in Jordan [12]. Li et al. examined that the efficiency of solar cells varies inversely with the temperature, and PV systems in hot regions operate more efficiently when the temperature is lowered [13]. Poudyal et al., above sea level, 72, 800, 1,350, 2,850 m, Biratnagar (26.45°N, 87.27°E), Pokhara (28.22°N, 83.32°E), Kathmandu (27.72°N, 85.32°E) and Lukla (26.69°N,86. 73°E), compared the solar radiation of the regions. According to the results, the total maximum solar radiation was observed in Biratnagar with 704.51 W/m2, Pokhara with 815.97 W/m2, Kathmandu with 777.27 W/m2 and Lukla with 914.03 W/m2. Also, during the year, average daily solar energy values of 4.95, 5.44, 5.19 and 4.61 kW h/m2 were found in Biratnagar, Pokhara Kathmandu and Lukla, respectively [14]. Madhavan and Ratnam, in their study in Gadanki, India, investigated the effect of solar radiation observed due to the annular lunar eclipse on the PV system [15]. Başay et al., in their study in Bursa, Turkey, investigated the effect of solar radiation, shading, and temperature on PV panel efficiency [16]. Singh et al. examined the efficiency of PV systems for six different regions in India. They were analyzed according to hot and humid, cold and humid, cold and sunny, cold and cloudy conditions [17]. Øgaarda et al. performed the efficiency analysis of a PV power plant with an installed power of 3.3 MW in Norway; snowy, variable weather conditions, temperature, low light and solar radiation changes were examined [18]. Liu et al. examined the efficiency of PV systems to be installed in buildings in areas with high solar radiation in terms of environmental benefits and economics [19]. Li et al. investigated the effects of the temperature difference between the lake and the land and the variation of air temperature and solar radiation on PV systems [20]. Del Pero et al. improved the efficiency of PV systems, apart from effects such as temperature, solar radiation, snow, pollution and wind speed, by performing an analysis taking into account the effect of rain [21]. Castillejo-Cuberos and Escobar studied the effects of short-term solar radiation variation on PV systems [22]. Lin and Ravindra investigated the panel sub-temperature effects of solar cells and the performance of solar panels in the range of 80–380°K [23]. Many studies have been carried out to analyze the efficiency of PV systems using different programs [24,25,26,27,28,29,30] and also in different purposes [31,32,33,34,35,36,37,38,39,40].

The photovoltaic systems (PVSOL) program allows users to carry out various analyzes based on real climate data. Therefore, these climate data vary according to the selected location. The climate data for the selected locations in this article are from 1991 to 2010. Using these climate data, the PVSOL program determines the average solar radiation, average temperature and shading conditions of the selected regions for the coming years. In line with these data obtained, twenty-year data analyses of the power plants to be established can be carried out. In this study, a 250 kWp (kilo watt peak) solar power plant with the same characteristics installed in four different geographical regions of Turkey was analyzed using the PVSOL program. Polycrystalline solar panels, which are widely used in the market, were used in the power plant models to be established. Annual electrical energy production efficiencies have been obtained from these established power plant models, depending on latitude and altitude. The effects of annual mean solar radiation value depending on latitude and temperature depending on altitude on electricity production were investigated. In addition, the cost analyzes of the established power plants were made, and the amortization and profit periods were compared.

2 Electricity generation from the sun

Electricity is produced from the sun by two methods: thermal and PV. In the thermal method, water vapor heated by the sun is used. In the PV method, electricity is produced according to the principle of solar rays moving free electrons in semiconductors. Monocrystalline, polycrystalline, thin film, flexible and transparent solar panels are used in PV systems.

3 Monocrystalline solar panels

The purity of the semiconductors used in monocrystalline solar panels is quite high. Sand and quartz found in nature contain abundant silicon semiconductors. Since the purity level of the sand is low, quartz is preferred in monocrystalline panel production. By processing quartz, 99% pure silicon is obtained, and this silicon is used in the production of monocrystalline solar panels.

4 Polycrystalline solar panels

The purity of the semiconductors used in polycrystalline solar panels is lower than that of the semiconductors used in monocrystalline solar panels. The images of the polycrystalline solar panels created without the use of purification methods are also not homogeneous and their costs are lower. Panel efficiencies are lower than monocrystalline panels due to their multi-crystalline structure.

5 Thin film solar panels

Thin-film solar panels are obtained by stacking different semiconductors in thin layers. Substances such as amorphous silicon, micromorph cell, copper indium and cadmium telluride are materials used in thin-film solar panel construction. Amorphous silicon is commonly preferred. Their efficiency is less than monocrystalline and polycrystalline panels.

6 Flexible solar panels

Flexible solar panels can be produced from monocrystalline, polycrystalline, or thin film. Such panels do not have an aluminum frame or tempered glass. Therefore, their weight is lower than other panels. Thanks to their flexible structures, they can be used easily in air, land and sea vehicles, bus stops and inclined lands.

7 Transparent solar panels

Although not widely used yet, such solar panels, which are in the research and development stage, can generate electricity from the sun with the help of their transparent structure and do not block the sun. Thanks to this feature, transparent solar panels will be able to produce more energy by using them in normal living spaces.

8 PVSOL program

With the PVSOL program, two-dimensional or three-dimensional design of PV systems can be made. In addition, devices, battery systems, electric vehicles and on-grid and off-grid network systems can be designed using climate and geographical data in the PVSOL program. In addition to these, visual and graphical data of the systems can be obtained by using PVSOL, a dynamic simulation program.

9 Solar power plants

Using the PVSOL program, four solar power plants with characteristics were designed in Turkey’s Erzurum, Çanakkale, Sinop and Hatay provinces. In each of these power plants, 1000 Solar Energy SA SE 250/60P Polycrystalline solar panels were used. The panels are modeled to be installed on a total area of 3,286 m2 with a width of 53 m and a length of 62 m. In order to carry out the installation and necessary maintenance, roads were built on the edges of the land of 50 cm and between the panels at 10 m intervals. The panel mounting angle is 30°. The distance between the panels is 1.636 m. Five inverters from the SMA Solar Technology AGI Sunny Tripower CORE 1 model are used to connect the system to the grid. The power of each power plant is 250 kWp. The power plant models are three-phase, with a voltage value of 230 volts between phase and neutral, connected to the grid (on-grid). The efficiency losses of the panels used in the system are taken into account. Incentive payments between 1 July 2021 and 31 December 2025, the value of 0.4 Ł/kWh to be applied for the solar-energy-based generation facility, have been used in the calculations. Electricity tariffs in the names of electricity distribution companies in the geographical region of each province where the power plant model was established were created at a value of 0.4 Ł/kWh, and payments were made over these tariffs. A monthly expense of 2,000 Ł has been shown to the power plants as security and operating expenses. The view of the power plant taken from the PVSOL program is shown in Figure 1.

Figure 1 
               The view of the solar plant from the south side.
Figure 1

The view of the solar plant from the south side.

9.1 Solar Energy Cell (SCE) Erzurum (East) power plant

Erzurum Energy Production Plant, which is the first of the four power plants to be established by SCE, has an average altitude of 1,900 m. Its coordinates are 39° north latitude and 41° east longitude. The annual average solar radiation value is 1,456 kW h/m2. The annual average temperature is 5°C. Figure 2 shows the information about the Erzurum power plant.

Figure 2 
                  PVSOL program screenshot of Erzurum power plant.
Figure 2

PVSOL program screenshot of Erzurum power plant.

9.2 SCE Çanakkale (West) power plant

The average elevation of the Çanakkale power plant, which is the second of the four power plants to be built by SCE, is 12 m. Its coordinates are 40° north latitude and 26° east longitude. The annual average solar radiation value is 1,443 kW h/m2. The annual average temperature is 15.3°C. Figure 3 shows the information about the Çanakkale power plant.

Figure 3 
                  PVSOL program screenshot of Çanakkale power plant.
Figure 3

PVSOL program screenshot of Çanakkale power plant.

9.3 SCE Sinop (North) power plant

The average elevation of the Sinop power plant, which is the third of the four power plants to be established by SCE, is 25 m. Its coordinates are 42° north latitude and 35° east longitude. The annual average solar radiation value is 1,506 kW h/m2. The annual average temperature is 14.8°C. Figure 4 shows the information about Sinop power plant.

Figure 4 
                  PVSOL program screenshot of Sinop power plant.
Figure 4

PVSOL program screenshot of Sinop power plant.

9.4 SCE Hatay (South) power plant

The average altitude of the Hatay power plant, which is the fourth of the four power plants to be established by SCE, is 89 m. Its coordinates are 36° north latitude and 36° east longitude. The annual average solar radiation value is 1,700 kW h/m2. The annual average temperature is 20°C. Figure 5 shows the information about the Hatay power plant.

Figure 5 
                  PVSOL program screenshot of Hatay power plant.
Figure 5

PVSOL program screenshot of Hatay power plant.

10 Data analyses

In this section, the data obtained from the power plant models created in the PVSOL program were analyzed. The effect of East and West power plants on electricity production in terms of altitude was been examined. On the other hand, the effect of latitude difference on electricity generation in North and South power plants was examined. Then, the cost data of four power plants were analyzed. As a result of this analysis, amortization and profit periods were compared.

10.1 Geographical locations of power plants

Table 1 shows the data for the four power plants to be established. When Table 1 is examined, according to the location selected in the PVSOL program, the average radiation amount, average temperature data and altitude information of the relevant region are displayed by using the actual data between 1991 and 2010. According to this information, since the Hatay power plant is closer to the equator in terms of latitude, the solar radiation value is higher than in other provinces. Therefore, the amount of electricity production and the income will increase. Since the Erzurum power plant is in a high-altitude region, the annual average temperature is lower. A low temperature will increase the efficiency of the panels.

Table 1

Data of the power plants to be established

Power plant data Power plants
Erzurum power plant Çanakkale power plant Sinop power plant Hatay power plant
Latitude (North) 39 40 42 36
Longitude (East) 41 26 35 36
Time zone UTC + 2 UTC + 2 UTC + 2 UTC + 2
Time period 1991–2010 1991–2010 1991–2010 1991–2010
Annual average radiation (kW h/m²) 1,456 1,443 1,506 1,700
Average annual temperature (°C) 5 15.3 14.8 20
Altitude (m) 1,900 12 25 89

10.2 Energy production data of power plants by month

Figure 6 shows the monthly energy production graph of the power plants. Because the value of solar radiation in winter months is less than in summer months, it is observed that the amount of electricity production decreases (Figure 6). The main factor in electricity production is the solar radiation value. However, due to special conditions such as different shading, weather changes and temperature difference, electricity generation close to each other was realized in some months, as in Erzurum and Çanakkale power plants, which have close solar radiation values. It has been clearly shown that when Hatay and Sinop power plants are located at different latitudes, and Çanakkale and Erzurum power plants are at different altitudes, the amount of energy they produce is not equal.

Figure 6 
                  Energy production graph of power plants by months.
Figure 6

Energy production graph of power plants by months.

10.3 Estimated earnings of power plants by years

Figure 7 shows the estimated earnings graph of the power plants over the years. Since the efficiency loss is taken into account in the solar panels used, it is observed that there is a decrease in electricity production depending on time. Therefore, the profit which is obtained decreases with time, as well. The Hatay power plant, which has a more efficient geographical location in terms of solar radiation, achieved the highest gain. Çanakkale and Erzurum power plants, which are at approximately the same latitude, have higher gains than Erzurum power plants, which are in a colder region. Although the solar radiation values of the regions close to the equator increase, it is shown that the Sinop power plant gains close to the Erzurum power plant owing to special environmental conditions.

Figure 7 
                  Estimated earnings graph of power plants by years.
Figure 7

Estimated earnings graph of power plants by years.

Table 2 contains the financial data of the power plants. When Table 2 is examined; total expenses and investment costs are equal in all power plants. At the end of the twenty-first year, it is shown that the least profit is obtained from Çanakkale power plant and the highest gain from the Hatay power plant.

Table 2

Income–expense table of the power plants

Power plants
Erzurum Çanakkale Sinop Hatay
Total earnings Ł2497276.68 Ł2398026.28 Ł2491731.00 Ł2835150.22
Total expenses Ł42000.00 Ł42000.00 Ł42000.00 Ł42000.00
Investment cost Ł2220000.00 Ł2220000.00 Ł2220000.00 Ł2220000.00

Figure 8 shows the graph of the financial status of the power plants at the end of the twenty-first year. Table 2 and Figure 8 show that Çanakkale, Erzurum, Sinop and Hatay power plants made a profit of 136026.28, 229731.00, 235276.68 and 541650.22 Ł, respectively.

Figure 8 
                  Financial position graphs of power plants at the end of the twenty-first year.
Figure 8

Financial position graphs of power plants at the end of the twenty-first year.

10.4 Annual specific gain data of power plants

Figure 9 shows the annual specific gain values of the four power plants. Although Erzurum and Çanakkale power plants are approximately at the same latitude, the effect of temperature difference due to altitude on panel efficiency is observed. Although Sinop and Hatay power plants are approximately at the same longitude, the Hatay power plant produced more electricity due to the high solar radiation on account of latitude.

Figure 9 
                  Annual specific earnings of power plants.
Figure 9

Annual specific earnings of power plants.

10.5 Shading data of power plants

Figure 10 indicates the percentage gain and loss account of the power plants due to shading. The power plants are designed with the same features in the PVSOL program. On the contrary, due to their geographical location, the power plants have different cloudiness rates during the day and the year. Therefore, the gain/loss due to shading is different for each plant. Losses of gains in Sinop, Erzurum, Çanakkale and Hatay power plants are seen as 13645.19, 10464.78, 6508.93 and 5940.31 Ł, respectively.

Figure 10 
                  Percentage gain losses of power plants due to shading.
Figure 10

Percentage gain losses of power plants due to shading.

11 Results

In this study, the effect of four different geographical regions in Turkey in electricity generation with solar panels on panel efficiency was analyzed using the PVSOL program. When the data obtained from the power plant models established in the PVSOL program are examined, it is seen that the change in latitude affects the solar radiation values. The amount of solar radiation is greater in areas close to the equator. Therefore, since the Hatay power plant, which is one of the selected regions, is closer to the equator, 13.78% more profit was obtained than the Sinop power plant. Changing the longitude values does not change the solar radiation values. However, since the temperature changes due to the altitude difference, it has been observed that the panel efficiency also changes. For this reason, the Erzurum power plant generated 4.13% more profit than the Çanakkale power plant. As a result, it is clearly seen that the PV systems to be installed in the region close to the equator and with high altitudes will make a profit in a shorter time. When investment and expenses are taken into account, a profit of 6.01% from the Çanakkale power plant, 10.15% from the Sinop power plant, 10.40% from the Erzurum power plant and 25.33% from the Hatay power plant. In addition to these, partial shading losses of modules were determined as 11.5% in Sinop power plant, 8.8% in Erzurum power plant, 5.7% in Çanakkale power plant and 4.4% in Hatay power plant. Considering these values, the efficiency of the panels can be further increased by using methods such as setting up the row spacing at the appropriate distance and avoiding the factors that will shade the modules.


There is no person or company to be acknowledged.

  1. Funding information: There is no external funding.

  2. Author contributions: All manuscripts were prepared by both authors.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: Data sharing is not applicable to this article as no new data were created or analyzed in this study.

  5. Informed consent statement: Not applicable.

  6. Ethical approval: The conducted research is not related to either human or animal use.


[1] Buni MJB, Al-Walie AAK, Al-Asadi KAN. Effect of Solar radiation on photovoltaic cell. Int Res J Adv Eng Sci. 2018;3:47–51.Search in Google Scholar

[2] Adak S, Cangi H, Yılmaz AS. Mathematical modeling and simulation of the output power of photovoltaic system based on temperature and radiation. Int J Eng Res Dev. 2019;11:316–27.Search in Google Scholar

[3] Usman Z, Tah J, Abanda H, Nche C. A critical appraisal of PV-systems performance. Buildings. 2020;10:1–22. 10.3390/buildings10110192.Search in Google Scholar

[4] Rodziewicz T, Rajfur M, Teneta J, Swislowski P. Modelling and analysis of the influence of solar spectrum on the effiency of photovoltaic modules. Energy Rep. 2021;7:564–74.Search in Google Scholar

[5] Koo C, Hong T, Lee M, Park HS. Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based. Environ Sci Technol. 2013;47:4829–39.10.1021/es303774aSearch in Google Scholar PubMed

[6] Demir HB, Özkan AO. The effect of temperature and zenith angle on panel power generation in photovoltaic panels. Necmettin Erbakan Univ J Sci Eng Konya Turk. 2019;1:1–9.Search in Google Scholar

[7] Bhol R, Dash R, Pradhan A, Ali SM. Environmental effect assessment on performance of solar PV panel. International Conference on Circuit, Power and Computing Technologies [ICCPCT], Nagercoil, India; 2015. p. 1–5. 10.1109/ICCPCT.2015.7159521Search in Google Scholar

[8] Dubey S, Narotam J, Seshadri B. Temperature dependent photovoltaic (PV) efficiency and its effect on PV production in the world - a review. Energy Proc. 2013;33:311–21.10.1016/j.egypro.2013.05.072Search in Google Scholar

[9] Ye Z, Nobre A, Reindl T, Luther J, Reise C. On PV module temperatures in tropical regions. Sol Energ. 2013;88:80–7.10.1016/j.solener.2012.11.001Search in Google Scholar

[10] Narkwatchara P, Ratanatamskul C, Chandrachai A. Effects of particulate matters and climate condition on photovoltaic system efficiency in tropical climate region. Energy Rep. 2020;6:2577–86.10.1016/j.egyr.2020.09.016Search in Google Scholar

[11] Bergin MH, Ghoroi C, Dixit D, Schauer JJ, Shindell DT. Large reductions in solar energy production due to dust and particulate air pollution. Environ Sci Technol Lett. 2017;4:339–44.10.1021/acs.estlett.7b00197Search in Google Scholar

[12] Al-Bashir A, Al-Dweri M, Al–Ghandoor A, Hammad B, Al–Kouz W. Analysis of effects of solar irradiance, cell temperature and wind speed on photovoltaic systems performance. Int J Energy Econ Policy. 2020;10:353–9.10.32479/ijeep.8591Search in Google Scholar

[13] Li W, Shi Y, Chen K, Zhu L, Fan S. A comprehensive photonic approach for solar cell cooling. ACS Photonics. 2017;4:774–82.10.1021/acsphotonics.7b00089Search in Google Scholar

[14] Poudyal KN, Bhattarai BK, Sapkota B, Kjeldstad B. Solar radiation potential at four sites of Nepal. J Inst Eng. 2011;8:189–97.10.3126/jie.v8i3.5944Search in Google Scholar

[15] Madhavan BL, Ratnam MV. Impact of a solar eclipse on surface radiation and photovoltaic energy. Sol Energy. 2021;223:351–66.10.1016/j.solener.2021.05.062Search in Google Scholar

[16] Başay V, Akyüz C, Yılmaz G. Factors determining the efficiency of the solar power plant established in forested and mid-mountain areas around Uludağ. Uludag Univ Eng Faculty J. 2019;24:181–91.10.17482/uumfd.444536Search in Google Scholar

[17] Singh D, Gautam AK, Chaudhary R. Potential and performance estimation of free-standing and buildingintegrated photovoltaic technologies for different climatic zones of India. Energy Built Environ. 2022;3:40–55.10.1016/j.enbenv.2020.10.004Search in Google Scholar

[18] Øgaarda MB, Riiseb HN, Hauga H, Sartoria S, Selj JH. Photovoltaic system monitoring for high latitude locations. Sol Energy. 2020;207:1045–54.10.1016/j.solener.2020.07.043Search in Google Scholar

[19] Liu Z, Zhang Y, Yuan X, Liu Y, Xu J, Zhang S, et al. A comprehensive study of feasibility and applicability of building integrated photovoltaic (BIPV) systems in regions with high solar irradiance. J Clean Prod. 2021;307:127240. 10.1016/j.jclepro.2021.127240.Search in Google Scholar

[20] Li P, Gao X, Li Z, Zhou X. Effect of the temperature difference between land and lake on photovoltaic power generation. Renew Energy. 2022;185:86–95.10.1016/j.renene.2021.12.011Search in Google Scholar

[21] Del Pero C, Aste N, Leonforte F. The effect of rain on photovoltaic systems. Renew Energy. 2021;179:1803–14.10.1016/j.renene.2021.07.130Search in Google Scholar

[22] Castillejo-Cuberos A, Escobar R. Understanding solar resource variability: an in-depth analysis, using Chile as a case of study. Renew Sustain Energy Rev. 2020;120:109664. 10.1016/j.rser.2019.109664.Search in Google Scholar

[23] Lin L, Ravindra NM. Temperature dependence of CIGS and perovskite solar cell performance: an overview. SN Appl Sci. 2020;2:1361. 10.1007/s42452-020-3169-2.Search in Google Scholar

[24] Aslam M, Lee SJ, Khang SH, Hong S. Two-stage attention over LSTM with Bayesian optimization for day-ahead solar power forecasting. IEEE Access. 2021;9:107387–98.10.1109/ACCESS.2021.3100105Search in Google Scholar

[25] Busson BO, Santos LO, Carvalho PCM, Carvalho CO. Experimental assessment and modeling of a floating photovoltaic module with heat bridges. IEEE Lat Am Trans. 2021;19:2079–86.10.1109/TLA.2021.9480150Search in Google Scholar

[26] Yu T, Ren C, Jia Y, Li J, Zhang J, Xu Y, et al. Photovoltaic panel temperature monitoring and prediction by raman distributed temperature sensor with fuzzy temperature difference threshold method. IEEE Sens J. 2021;21:373–80.10.1109/JSEN.2020.3015508Search in Google Scholar

[27] Haddad S, Lekouaghet B, Benghanem M, Soukkou A, Rabhi A. Parameter estimation of solar modules operating under outdoor operational conditions using artificial hummingbird algorithm. IEEE Access. 2022;10:51299–314.10.1109/ACCESS.2022.3174222Search in Google Scholar

[28] Milosavljević DD, Kevkić TS, Jovanović SJ. Review and validation of photovoltaic solar simulation tools/software based on case study. Open Phys. 2022;20:431–51.10.1515/phys-2022-0042Search in Google Scholar

[29] Sun Q, Long J, Li X, Dai P, Zhang Y, Xuan J, et al. The diffusion effect of copper on the flexible GaInP/GaAs solar cells. IEEE Electron Device Lett. 2022;43:584–7.10.1109/LED.2022.3156377Search in Google Scholar

[30] Khorashad LK, Argyropoulos C. Unraveling the temperature dynamics and hot electron generation in tunable gap-plasmon metasurface absorbers. Nanophotonics. 2022;48:1–6.10.1515/nanoph-2022-0048Search in Google Scholar

[31] Turna IB, Zuhal Er. Performance monitoring of different types of photovoltaic cells. Emerg Mater Res. 2022;11(1):60–6. 10.1680/jemmr.20.00251.Search in Google Scholar

[32] Kayiran HF. Numerical analysis of composite discs with carbon/epoxy and aramid/epoxy materials. Emerg Mater Res. 2022;11(1):155–9. 10.1680/jemmr.21.00052.Search in Google Scholar

[33] Akkurt I, Uyanik NA, Günoğlu K. Radiation dose estimation: An in vitro measurement for Isparta-Turkey. Int J Comput Exp Sci Eng. 2015;1(1):1–4. 10.22399/ijcesen.194376.Search in Google Scholar

[34] Al-Sarraya E, Akkurt İ, Günoğlu K, Evcin A, Bezir NÇ. Radiation shielding properties of some composite panel. Acta Phys Polonica A. 2017;132(3):490–2. 10.12693/APhysPolA.132.490.Search in Google Scholar

[35] Hanfi MY, Sayyed MI, Lacommeet E, Akkurtf I, Mahmoud KA. The influence of MgO on the radiation protection and mechanical propertiesof tellurite glasses. Nucl Eng Technol. 2021;53(6):2000–10. 10.1016/ in Google Scholar

[36] Ayten Uyanık N, Uyanık O, Akkurt İ. Micro-zoning of the natural radioactivity levels and seismic velocities of potential residential areas in volcanic fields: the case of Isparta (Turkey). J Appl Geophys. 2013;98:191–204. 10.1016/j.jappgeo.2013.08.020.Search in Google Scholar

[37] Jawad AA, Demirkol N, Gunoğlu K, Akkurt I. Radiation shielding properties of some ceramic wasted samples. Int J Env Sci Technol. 2019;16:5039–42. 10.1007/s13762-019-02240-7.Search in Google Scholar

[38] Akkurt I, Günoğlu K. Natural radioactivity measurements and radiation dose estimation in some sedimentary rock samples in Turkey. Sci Technol Nucl Install. 2014;2014:950978. 10.1155/2014/950978.Search in Google Scholar

[39] Gunoglu K, Özkavak HV, Akkurt İ. Evaluation of gamma ray attenuation properties of boron carbide (B4C) doped AISI 316 stainless steel: Experimental, XCOM and Phy-X/PSD database software. Mater Today Commun. 2021;29:102793. 10.1016/j.mtcomm.2021.102793.Search in Google Scholar

[40] Kulali F, Akkurt I, Özgür N. The Effect of meteorological parameters on radon concentration in soil gas. Acta Phys Polonica A. 2017;132(3II):999–1001. 10.12693/APhysPolA.132.999.Search in Google Scholar

Received: 2022-06-01
Revised: 2022-06-22
Accepted: 2022-06-30
Published Online: 2022-08-17

© 2022 Muhammet Demirkiran and Abdulhakim Karakaya, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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