Bayesian Forecasting in Senegal's Power Grid: Asymptotic Analysis and Identifiability Checks

The power grid in Senegal is a critical infrastructure subject to frequent disruptions due to various factors including weather and maintenance issues. A Bayesian hierarchical model was constructed using historical data from Senegal's power grid. The model accounts for temporal dependencies and external covariates affecting grid stability. Identifiability checks were performed to ensure reliable parameter estimation. The asymptotic analysis of the proposed model showed that the forecasting accuracy improved as more data points were incorporated, with a significant reduction in prediction errors. This study validated the effectiveness and robustness of the developed Bayesian model for predicting power grid failures in Senegal. The model's ability to identify key parameters contributing to reliability is noteworthy. The findings suggest that further research should focus on integrating real-time data into the forecasting framework to enhance its predictive capabilities. Bayesian Forecasting, Power Grid Stability, Identifiability Checks, Senegal The analytical core is yₜ=F (xₜ;) with =argmin_L (), and convergence is established under standard smoothness conditions.