Abstract
{ "background": "Power-distribution systems in Ghana face persistent challenges with equipment reliability and yield losses. Existing analytical methods often fail to adequately account for the hierarchical structure of network data and inherent uncertainties in performance measurement.", "purpose and objectives": "This study presents a methodological evaluation of a Bayesian hierarchical model designed to measure and improve yield within the nation's power-distribution equipment systems. The objective is to assess the model's efficacy in quantifying performance gains and identifying influential factors.", "methodology": "A Bayesian hierarchical model was developed, formalised as $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau^2)$, where $y{ij}$ is the yield for equipment $i$ in region $j$. The model was applied to operational data from multiple distribution networks. Inference was based on posterior distributions with 95% credible intervals.", "findings": "The methodological evaluation demonstrated that the model successfully quantified yield improvements, isolating regional random effects from equipment-level covariates. A key finding was a central estimate of a 7.2% yield improvement attributable to a specific maintenance intervention, with a 95% credible interval of [5.1%, 9.3%]. The model showed robust performance in handling sparse data at the sub-station level.", "conclusion": "The Bayesian hierarchical modelling approach provides a statistically rigorous framework for evaluating yield improvement in power-distribution systems, offering superior handling of uncertainty and data structure compared to conventional methods.", "recommendations": "Adoption of this modelling framework is recommended for ongoing performance monitoring and resource allocation. Future work should integrate real-time sensor data to enhance model predictive capability.", "key words": "Bayesian inference, hierarchical model, distribution networks, equipment yield, reliability engineering, probabilistic modelling", "contribution statement": "This paper provides a novel methodological framework for power-system yield analysis, introducing a tailored Bayesian hierarchical