What question did this study set out to answer?

The objective is to assess the reliability of power distribution equipment systems in Rwanda using time-series forecasting models.

March 7, 2026Open Access

Power-Distribution Equipment Systems Reliability Assessment in Rwanda Using Time-Series Forecasting Models

Key Points

The objective is to assess the reliability of power distribution equipment systems in Rwanda using time-series forecasting models.
Used the ARIMA model for forecasting power supply interruptions.
Employed robust standard errors to quantify prediction uncertainty.
Analyzed historical data to predict potential system failures.
Predicted system failures with an accuracy rate of 85%.
Demonstrated the effectiveness of the time-series approach in forecasting interruptions.
Provided insights for implementing better maintenance strategies.

Abstract

Power distribution equipment (PDE) systems in Rwanda face challenges related to reliability due to varying environmental conditions and infrastructure age. The study employs ARIMA (AutoRegressive Integrated Moving Average) model for forecasting power supply interruptions. Robust standard errors are used to quantify uncertainty around predictions. A significant proportion of PDE system failures were predicted with an accuracy rate of 85% based on historical data, indicating the effectiveness of the time-series approach. The ARIMA model successfully forecasts future power interruptions in Rwanda's PDE systems, providing actionable insights for maintenance and upgrades. Implementing preventive maintenance strategies informed by predictive models can enhance system reliability and reduce operational disruptions. Power Distribution Equipment, Time-Series Forecasting, Reliability Assessment, ARIMA Model, Predictive Maintenance The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

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