Weather and air pollution influences on solar energy performance in West Africa: A Bayesian nonlinear mixed-effects approach

Abstract. In the context of the global shift toward an energy transition in which solar power plays an increasingly significant role, solar resource forecasting has drawn considerable attention from researchers and stakeholders. Accurate predictions enable better planning, controlled integration of solar energy and improved project profitability. In this regard, understanding how individual predictors influence solar radiation is crucial for selecting relevant inputs, reducing computational costs and enhancing model performance. However, this relationship is still frequently modelled as linear, an assumption that neglects the complex, nonlinear and hierarchical interactions that characterize atmospheric processes. In this study, a Bayesian mixed effects model was developed to assess how meteorological variables and air pollution affect solar energy generation, using ground-based observations and satellite-derived atmospheric data. The Bayesian framework incorporates prior knowledge, quantifies uncertainty and captures spatiotemporal variability. The results show that the proposed framework captures the non linear effects of predictors on solar radiation and outperforms generalized linear and additive models. Significant station-level random effects highlight the importance of local environmental characteristics in multi-site modeling, suggesting architectures like graph neural networks may be advantageous. Temperature, humidity and cloud cover are the primary drivers of global horizontal irradiance, with PM_2.5 showing a notable impact under cloud-free conditions. Addressing this gap in nonlinearity is significant because capturing multi-scale dependencies is essential not only for advancing predictive modelling frameworks, but also for improving the physical interpretability of solar radiation dynamics and enabling more robust integration of solar resources into energy systems. The findings extend to similar tropical climates and the model can be adapted to diverse regions and data sources to support solar energy optimization.