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

Abstract

To implement the European Union (EU)-Africa Green Energy Initiative in Cameroon to boost the renewable energy sector, we model the performance of a 500 W monocrystalline solar panel in major cities of Cameroon located in different climatic zones to select the best location for the installation of a solar farm. We also evaluate the contribution of seasonal and weather variability to the amount and stability of power generated by the panel using the artificial neural network (ANN). The ANN model was used to train and test the ERA5 hourly data for Bamenda. The model was then used to estimate Photovoltaic (PV) output in Douala, Yaounde, Ngaoundere, Garoua, and Maroua with a mean absolute error of 4.109 × 10−5, 4.699 × 10−5, 3.563 × 10−5, 3.106 × 10−5, and 3.083 × 10−5 kW, respectively. The results show that the ANN can capture the influence of weather variability on the generated output power. Cloud cover and rainfall are found to negatively affect the amount and stability of generated power in the lower latitude cities of Douala and Yaounde compared to the northern cities, with these effects being stronger in the rainy season than in the dry season. Garoua followed by Maroua are proving to be the best locations for installing a solar park in terms of the amount and stability of electricity generated throughout the year. The Cameroonian government, its EU partners, and other stakeholders involved in the development of solar energy in the country will be able to use the results of this study for better decision-making.