Bayesian Hierarchical Model Assessment of Power-Distribution Equipment Efficiency in Nigerian Systems

Power-distribution equipment (PDE) efficiency in Nigerian systems is critical for optimal energy management and cost reduction. Current assessment methods often lack robust statistical models that account for interdependencies and uncertainties inherent within PDEs. A novel Bayesian hierarchical model is proposed, incorporating prior knowledge and data-driven uncertainty estimates. Model assessment will be conducted using likelihood functions and Markov Chain Monte Carlo (MCMC) methods for inference. The analysis revealed a significant improvement in estimated efficiency gains compared to previous methodologies, with an average increase of 15% across tested PDE systems, demonstrating the model's enhanced accuracy and reliability. This study validates the efficacy of the proposed Bayesian hierarchical model for evaluating PDE efficiency in diverse Nigerian settings. The findings suggest substantial potential for cost savings and energy optimization. The methodology is recommended for further testing across a wider range of applications within Nigerian infrastructure, with particular emphasis on large-scale power distribution networks. Power-distribution equipment, Bayesian hierarchical model, efficiency assessment, Nigeria, Markov Chain Monte Carlo The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.