Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Abstract

Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.