Assessment of the Impact of Climatic Variations on the Photovoltaic Potential of Kankan (Guinea) Using NASA POWER Data and Artificial Neural Network (2000–2025)

West Africa benefits from strong solar resources, yet in Guinea the influence of climate change on photovoltaic availability has received little attention. This work evaluates the effects of climate change on the photovoltaic potential of Kankan (Guinea) using daily data from the NASA POWER database for 2000-2025. The study focuses on global solar irradiation, air temperature, relative humidity, and wind speed. A semi-empirical model is used to estimate daily photovoltaic potential, and trends are analyzed with linear regression and the non-parametric Mann-Kendall test. In parallel, a multilayer perceptron artificial neural network (MPANN) is developed to reproduce the estimated photovoltaic potential from the selected climatic inputs. The analysis shows significant declines in air temperature (-0.0559°C year -1 ) and wind speed (-0.0077 ms -1 year -1 ), together with a significant rise in relative humidity (+0.6249% year -1 ). Global solar irradiation presents no statistically significant trend over the study period. Despite these changes, the photovoltaic potential remains broadly stable, with a small and statistically non-significant decrease of -0.0152 kWh day -1 year -1 (p = 0.186 > 0.05). The MPANN delivers very high predictive performance, with the coefficient of determination of 0.99, the mean absolute error (MAE) of 0.0266 kWh day -1 , the root mean square error (RMSE) of 0.0478 kWh day -1 , and the mean absolute percentage error (MAPE) of 0.134%. Overall, the climatic shifts observed in Kankan during the last twenty-six years have not significantly altered the region’s photovoltaic potential. This stability supports the suitability of solar energy as a sustainable option to meet Guinea’s growing energy needs. The study also highlights the usefulness of artificial intelligence methods for assessing and forecasting renewable energy resources in tropical regions.