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%.

A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

“…Moreover, the second order autoregressive-SEM, as measured by MAPE and RMSE, outperformed other models, ARIMA model, gray model, ANN model, BP model, and ML model, used by the government as a tool for formulating policies for Thailand in the past. Hence, the second order autoregressive-SEM is found suitable to use for a long-term forecasting (2020-2035), as claimed by Oh and Shin [16] under the title of A Study on the Relationship between Analysts Cash Flow Forecasts Issuance and Accounting Information, Jiang et al [41] under the title of Comparison of Forecasting India's Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model, Wang et al [42] under the title of Prediction of the Energy Demand Trend in Middle Africa-A Comparison of MGM, MECM, ARIMA and BP Model, Ma et al [43] under the title of Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models, Boyd et al [44] under the title of Influent Forecasting for Wastewater Treatment Plants in North America, Al-Douri et al [45] under the title of Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans, and Alsharif et al [46] under the title of Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea.…”

“…The study indicates a 5% growth in energy consumption from 2017 to 2030. Wang, Zhan and Li [42] investigated and forecast energy demand in Middle Africa for 14 years (2017-2030). Their forecast projects a growth rate of 5.37% in energy demand.…”

Energy price modeling in sub-Saharan Africa: a systematic literature review

“…For example, in South Africa, the country's energy consumption has been researched and predicted, with ANN used as one of the statistical approaches [29,30]. ANN models, including the MGM and MCEM models, have been used to estimate energy demand in Central African countries [31]. The result has been more efficient demand prediction, although the incorporation of price prediction has often been elusive.…”

Forecasting Gasoline Demand in Indonesia Using Time Series

“…Reliable gasoline demand forecasting is essential for petroleum supply chain planning. Many studies of various scopes have been conducted on energy demand forecasting, including energy as a whole (De Vita et al, 2006;Sözen and Arcaklioglu, 2007;Lee and Tong, 2012;Barak and Sadegh, 2016;Rehman et al, 2017;Ozturk and Ozturk, 2018;Wang et al, 2018;Wang et al, 2019;Li and Zhang, 2019), electricity (González-Romera et al, 2008;Maçaira et al, 2015;Hussain et al, 2016;Ryu et al, 2017;Oliveira and Oliveira, 2018;McNeil et al, 2019;Jiang et al, 2020), petroleum (Houri and Baratimalayeri, 2008;Sa'ad, 2009;Azadeh et al, 2010;Ma et al, 2012;Melikoglu, 2013;Barde, 2014;Chai et al, 2016;Akhmad and Amir, 2018;Sapnken et al, 2018;Oliskevych et al, 2018), natural gas (Szoplik, 2015;Akpinar and Yumusak, 2016;Karabiber and Xydis, 2020), or solar and wind energy (Alsaedi et al, 2019).…”