This Data Descriptor focuses on evaluating power-distribution equipment systems in Kenya by applying time-series models to forecast adoption rates. A time-series model was employed to analyse data on power-distribution equipment adoption over a specific period. The study utilised statistical software to forecast future trends based on historical data. The analysis revealed that there was a significant increase in the adoption rate for new energy-efficient equipment from to , with a growth exceeding 30%. This study demonstrated the effectiveness of time-series models in forecasting power-distribution equipment adoption rates and provided insights into future trends. Further research should explore other socio-economic factors that may influence adoption rates to enhance predictive accuracy. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.