Empirical correlations for temperature, and output power of photovoltaic panel under different cooling conditions using porous media

Fossil fuels have traditionally been central to global energy generation. However, the depletion of these resources has urged the need for renewable alternatives. This challenge has driven the use of photovoltaics (PV) for electrical energy generation. However, when PV panels are used, their temperature rises, resulting in reduced output energy and electrical efficiency. The primary objectives of this study are to reduce the operating temperature of the PV panel by using porous media cooling. To achieve these objectives, both experimental and numerical studies were undertaken. The experimental work was divided into indoor and outdoor experiments, whereby porosity size ranged from 0.35 to 0.5 and volumetric flow rates ranged from 1 to 4 L/min were considered. These experiments employed a cooling channel mounted on the back of the PV panel. In the numerical studies, a mathematical model was first utilized to evaluate temperature changes and output power using thermal circuit representation. Additionally, ANSYS software was used in the study to investigate the effect of various other parameters involved in PV cooling by porous media. These parameters include the cooling channel height, as well as the shape of the cooling channel which was achieved by varying the channel height at the liquid outlet. Moreover, various types of coolants, which were water, chemical alcohol, engine oil, and different types of porous media, including gravel, marble, flint, and sandstone were further considered in this study. Machine learning (AdaBoost algorithm) was also considered to weigh the parameters influencing PV cooling. Empirical correlations were then developed to predict the temperature and output power of PV panels to complete the study. Based on the indoor experiments, the best-case scenario resulted in a reduced surface temperature of 38.7°C, with an enhanced output power of 19 W, which was achieved with a porosity size of 0.35 and a volumetric flow rate of 2 L/min. For the outdoor test, the optimal results were attained with the same porosity size and volumetric flow rate, with a PV surface temperature of 39.4°C. The comparison between the experimental and mathematical results yielded a maximum error of only 5.6%. For the present study using ANSYS, the PV temperature was reduced to 36.6°C, which was achieved with a cooling channel height of 5 mm at the inlet and 3 mm at the outlet, employing water as the coolant and sandstone as the porous media with a porosity size of 0.35 and a volume flow rate of 2 L/min. Machine learning identified porosity size as the primary parameter influencing the PV cooling efficiency, accounting for 25% of all parameters. Finally, the comparison between the experimental results and empirical correlations produced a maximum error of only 4.7%.

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