Time-Series Forecasting Model Evaluation for Efficiency Gains in Power-Distribution Equipment Systems in South Africa: An Analytical Methodology

This study focuses on evaluating time-series forecasting models to measure efficiency gains in power-distribution equipment systems within South Africa's engineering sector. A hybrid ARIMA-GARCH model was employed, incorporating historical power consumption data from South African substations. Model parameters were optimised using Bayesian inference with robust standard errors to account for uncertainty. The time-series forecasting model demonstrated an accuracy rate of 85% in predicting future power demands, with a confidence interval indicating the reliability of these predictions. The hybrid ARIMA-GARCH model proved effective in forecasting efficiency gains in South African power-distribution systems, providing actionable insights for system optimization. Further research should investigate broader applications and explore additional data sources to enhance predictive accuracy. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.