Assessing the performance of a monocrystalline solar panel under different tropical climatic conditions in Cameroon using artificial neural network.

This article focuses on assessing the solar energy potential in Cameroon using an artificial neural network (ANN) to model the performance of a 500 W monocrystalline photovoltaic (PV) panel across six major cities representing different climatic zones. Utilizing ERA5 reanalysis weather data from the European Center for Medium-Range Weather Forecasts (ECMWF), the study evaluates how seasonal and weather variability affect solar power output and stability, finding that northern cities Garoua and Maroua offer the most consistent and highest solar energy generation year-round. The ANN model demonstrated high accuracy in predicting PV output, capturing the negative impacts of cloud cover and rainfall especially in lower latitude cities like Douala and Yaounde during the rainy season. The findings provide valuable insights for the Cameroonian government, its European Union partners, and stakeholders to optimize solar farm site selection and improve renewable energy integration and grid management in Cameroon.