Off-grid community systems in Kenya have evolved significantly over recent years, yet their cost-effectiveness remains a subject of debate and evaluation. A time-series forecasting model was developed using historical data on electricity usage, cost structures, and technological advancements within off-grid community systems. Robust statistical techniques were employed to account for uncertainties in the data. The forecast indicated a steady decline in per capita system costs over five years, with an average reduction of approximately 10% annually, reflecting improved efficiency and economies of scale. The time-series model provided valuable insights into cost-effectiveness trends but acknowledged limitations such as data variability and potential technological obsolescence. Further research should focus on incorporating real-time data inputs to enhance the predictive accuracy of future forecasting models. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.