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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model