Abstract
{ "background": "The long-term financial sustainability of power-distribution infrastructure is a critical engineering challenge, particularly in contexts of constrained capital expenditure and ageing assets. Existing asset management models often lack integrated, forward-looking cost-effectiveness analyses tailored to specific national grid conditions.", "purpose and objectives": "This case study develops and methodologically evaluates a novel time-series forecasting model to measure the cost-effectiveness of power-distribution equipment systems. The objective is to provide a robust, data-driven tool for long-term capital planning and asset replacement strategy.", "methodology": "A case study methodology was employed, utilising historical operational and cost data from a major utility. The core analytical engine is a vector autoregression (VAR) model, specified as $yt = A1 y{t-1} + \\dots + Ap y{t-p} + \\epsilont$, where $y_t$ is a vector of cost and performance metrics. Model robustness was assessed using heteroskedasticity-consistent standard errors.", "findings": "The methodological evaluation confirms the model's utility for forecasting total cost of ownership. A key forecast indicates that, under current investment trends, the cost per unit of reliability is projected to increase by approximately 18–25% over the forecast horizon, with a 95% prediction interval of [16.2%, 27.1%]. The model identifies transformer populations as the most critical cost-driver.", "conclusion": "The developed forecasting model provides a technically sound and methodologically rigorous framework for evaluating the cost-effectiveness of distribution assets. It successfully integrates historical performance data into a predictive financial planning tool.", "recommendations": "Utilities should adopt similar integrated forecasting models for strategic asset management. Future work should incorporate real-time sensor data to transition from time-based to condition-based forecasting, enhancing predictive accuracy.", "key words": "asset management, cost forecasting, distribution infrastructure, vector autoregression, engineering economics", "contribution statement": "This paper presents a novel integrated VAR modelling framework that uniquely combines technical