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This study investigates the performance of photovoltaic (PV) modules cooled by L-shaped aluminum fins using weather data to predict power output. Two modeling approaches, multiple linear regression (MLR) and a multilayer perceptron (MLP) neural network, were employed, with the MLP model demonstrating superior accuracy in estimating power generation. The findings highlight the significant correlation between various weather parameters and PV output, emphasizing the importance of effective cooling methods for

Author

Tamara Salah

Language

EN