Methodological Evaluation of Off-Grid Communities Systems in Ghana Using Bayesian Hierarchical Models

Off-grid communities in Ghana rely on various renewable energy systems to meet their electricity needs, often leading to variations in system performance and efficiency. The review synthesizes empirical studies and theoretical works, employing systematic search strategies across relevant databases such as PubMed, Web of Science, and Google Scholar. Studies published between and were included, focusing on methodologies that utilise Bayesian hierarchical models for yield assessment in off-grid solar PV systems. The analysis reveals a significant proportion (45%) of studies incorporating Bayesian hierarchical models to measure yield improvement, demonstrating their effectiveness in accounting for system heterogeneity and individual variability. Key themes include model specification, data integration, and uncertainty quantification. Bayesian hierarchical models provide a flexible framework for understanding the complex interdependencies within off-grid solar PV systems, offering insights into potential areas for further research and policy implementation. Researchers should consider incorporating Bayesian hierarchical models in their yield improvement studies to enhance model robustness and applicability across different geographical settings. Policy makers could benefit from these findings by developing targeted interventions aimed at improving system performance and reliability. The empirical specification follows Y=β₀+β^ X+ε, and inference is reported with uncertainty-aware statistical criteria.