Methodological Evaluation and Time-Series Forecasting for Efficiency Gains in Senegal's Power-Distribution Infrastructure

{ "background": "Chronic inefficiencies in power-distribution networks, characterised by high technical and commercial losses, present a significant barrier to sustainable development in many nations. A rigorous, data-driven methodology for evaluating equipment performance and forecasting efficiency gains is required to inform infrastructure investment.", "purpose and objectives": "This study aims to develop and apply a novel methodological framework for evaluating power-distribution equipment systems, with the objective of constructing a robust time-series forecasting model to quantify potential efficiency gains within a national grid.", "methodology": "A comprehensive dataset of operational parameters from primary substations was analysed. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as \Δ yt = \α + \∑i=1ᵖ\ \Δ yt-i + \∑j=1q\ \εt-j + \∑k=1ʳ\ Xk,t + \, where $Xk$ represents exogenous technical variables. Model robustness was verified using heteroskedasticity-consistent standard errors.", "findings": "The ARIMAX(2,1,2) model demonstrated strong predictive capability, indicating that targeted upgrades to ageing circuit-breakers and transformers could reduce aggregate technical losses by an estimated 18.5% (95% CI: 16.2% to 20.7%) over a five-year forecast horizon, conditional on sustained investment.", "conclusion": "The methodological framework provides a statistically rigorous tool for infrastructure assessment, confirming that strategic, data-led interventions in specific equipment categories can yield substantial and quantifiable improvements in distribution efficiency.", "recommendations": "Utility planners should adopt this forecasting methodology for long-term infrastructure planning. Initial investment should be prioritised for substations identified as having the highest marginal return on loss reduction.", "key words": "power distribution, time-series forecasting, infrastructure efficiency, ARIMAX, technical losses, grid modernisation",