Time-Series Forecasting Model Evaluation for Cost-Effectiveness Assessment of Power-Distribution Equipment Systems in Senegal

The efficient operation of power distribution systems is crucial for sustainable energy supply in developing countries like Senegal. Current models often overlook time-series data analysis, which can lead to suboptimal equipment selection and maintenance strategies. A hybrid ARIMA-GARCH model was developed and applied to historical electricity consumption data from Senegal. Model robustness was assessed through cross-validation, with uncertainty quantified using bootstrapping techniques. The forecasted demand trend indicated a steady increase over the next five years, necessitating proportional upgrades in equipment capacity. This finding suggests a need for more frequent maintenance schedules to prevent unexpected failures. The hybrid ARIMA-GARCH model provided reliable forecasts of electricity demand and cost savings potential, facilitating informed decision-making in power distribution system management. Senegalese utilities should implement the recommended equipment upgrades and scheduling protocols based on this study's findings to enhance operational efficiency and reduce long-term costs. The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.