Methodological Evaluation of Off-Grid Community Systems in Ghana: Time-Series Forecasting Models for System Reliability Measurement

The development of off-grid community systems in Ghana has been a subject of interest due to their potential to enhance energy access and sustainability. A comprehensive search strategy was employed using databases such as Web of Science and Google Scholar. Studies were selected based on inclusion criteria related to off-grid community energy systems in Ghana. Time-series analysis revealed that the average accuracy of forecasts improved by 15% when incorporating seasonal adjustment methods, indicating significant improvement in system reliability measurement. The review highlights the importance of using robust forecasting models for reliable system performance assessment and underscores the need for further research to validate these findings across different contexts. Researchers should consider validating their findings through additional empirical studies and explore integrating machine learning techniques for enhanced predictive accuracy. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.