A Time-Series Forecasting Model for Risk Reduction in Nigerian Power-Distribution Equipment: A Policy Analysis for Asset Management (2000–2026)

{ "background": "Chronic underinvestment and reactive maintenance have precipitated a crisis in Nigeria's power-distribution infrastructure, leading to frequent equipment failure, high technical losses, and unreliable supply. Effective policy for asset management requires robust, forward-looking tools to quantify risk and prioritise interventions.", "purpose and objectives": "This policy analysis develops and evaluates a novel time-series forecasting model to measure potential risk reduction in power-distribution equipment. It aims to provide a methodological framework for evidence-based asset management policy, enabling the proactive allocation of maintenance and replacement resources.", "methodology": "A quantitative analysis was conducted using historical failure and maintenance data for transformers and switchgear. The core model is a seasonal autoregressive integrated moving average (SARIMA) with exogenous variables (SARIMAX), specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont + \\beta Xt$, where $X_t$ includes climatic and load stress factors. Model parameters were estimated using maximum likelihood, with forecasts evaluated for statistical robustness.", "findings": "The model forecasts a 22% reduction in the annual probability of catastrophic transformer failure under a proactive replacement policy informed by the risk projections, with a 95% confidence interval of [18%, 26%]. The analysis identifies load growth during peak demand periods as the most significant exogenous driver of equipment stress, outweighing ambient temperature effects.", "conclusion": "The proposed forecasting model provides a technically sound basis for transforming asset management from a reactive to a predictive regime. It demonstrates that quantified risk reduction is achievable through data-driven policy.", "recommendations": "Policymakers and distribution companies should institutionalise the integration of time-series forecasting into asset management strategies. Regulators should consider permitting capital expenditure recovery linked to demonstrated, forecasted risk reduction, creating a financial incentive for proactive investment.", "key words": "asset management, distribution infrastructure, forecasting, policy analysis, risk reduction, SARIMA, time-series",