What question did this study set out to answer?

This research aims to evaluate the reliability of off-grid community energy systems in Ghana using time-series forecasting models.

February 24, 2026Open Access

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

Key Points

This research aims to evaluate the reliability of off-grid community energy systems in Ghana using time-series forecasting models.
Comprehensive search strategy using databases like Web of Science and Google Scholar
Selection of studies based on inclusion criteria for off-grid community energy systems
Utilization of time-series analysis to assess forecasting accuracy
Incorporation of seasonal adjustment methods in forecast models
Evaluation of model performance using out-of-sample error.
Forecast accuracy improved by 15% with seasonal adjustment methods
Highlighting the importance of robust forecasting for system reliability
Need for further empirical studies to validate findings in different contexts

Abstract

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 =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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