Off-grid energy systems are critical for rural development and agricultural productivity in sub-Saharan Africa, yet their reliability is often compromised by variable renewable resources and demand. Effective risk management requires robust forecasting to anticipate supply shortfalls and mitigate their impact on agrarian livelihoods. This study presents a methodological evaluation of a novel time-series forecasting model designed to quantify risk reduction in off-grid energy systems supporting rural Ethiopian communities, with a focus on agricultural applications. We developed a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model, specified as (B) (Bˢ) ᵈₛD yₜ = (B) (Bˢ) ₜ + Xₜ, using high-frequency system performance data. Model parameters were estimated via maximum likelihood, and forecasting accuracy was evaluated against held-out data using Diebold-Mariano tests with robust standard errors. The model reduced forecasting error for daily energy shortfalls by 34% compared to a naive seasonal benchmark (p < 0. 01). The primary risk reduction mechanism was improved anticipation of shortfalls during critical ploughing and irrigation periods, enabling proactive diesel generator deployment. The proposed forecasting methodology provides a statistically rigorous tool for quantifying and mitigating operational risks in decentralised renewable energy systems, directly supporting agricultural resilience. Energy system planners should integrate similar forecasting models into operational management protocols. Further research should focus on embedding these models in low-cost, automated control systems for real-time risk mitigation. energy security, rural electrification, agricultural productivity, predictive modelling, sustainable development This paper introduces a novel application of the SARIMAX framework for quantifying energy risk reduction, demonstrating its utility through a significant improvement in forecast accuracy for an off-grid system in a key agrarian region.
Tadesse et al. (Fri,) studied this question.