Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models

Abstract

South Africa’s energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of South Africa’s energy consumption during 1998–2016, Autoregressive Integrated Moving Average (ARIMA) model, nonlinear grey model (NGM) and nonlinear grey model–autoregressive integrated moving average (NGM-ARIMA) model are adopted to predict South Africa’s energy consumption during 2017–2030. After using these NGM, ARIMA and NGM-ARIMA, the mean absolute percent errors (MAPE) are 2.827%, 2.655% and 1.772%, respectively, which indicates that the predicted result has very high reliability. The prediction results show that the energy consumption in South Africa will keep increasing with the growth rate of about 7.49% in the next 14 years. This research result will provide scientific basis for the policy adjustment of energy supply and demand in South Africa and the prediction techniques used in the research will have reference function for the energy consumption study in other African countries.

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

“…Nepal et al have combined the clustering and the ARIMA model toward electricity load forecasting,; the result has proved that the proposed approach has provided improved accuracy as well as superior performances than that using the ARIMA model alone [14]. The combined method which consists of the ARIMA and NGM methods, namely, the NGM-ARIMA model has been put forward by Ma et al aimed at accurately predicting South Africa's energy consumption in 2017-2030 [15]; the highest prediction accuracy was achieved by the NGM-ARIMA model, and the prediction result is more close to the actual energy consumption compared to the single ARIMA and NGM model. Gulay and Duru have combined three different models: ARDL (autoregressive distributed lag model), EMD (empirical mode decomposition), and ANN (artificial neural network) for the predictive analytics of energy systems and prices; the proposed hybrid forecasting algorithms provided better results by improving the forecasting accuracy [16].…”

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

“…There is a large plethora of literature available on the issue of energy consumption forecasting. Many studies used methods for forecasting energy consumption, e.g., [5][6][7][8][9][10][11][12][13][14][15], and some studies were forecasted by comparing the approach with some other methods. On the other hand, some studies used the grey methods for energy consumption forecasting.…”

“…There are very few studies that evaluated energy consumption for BRICS by using the model. Some studies forecasted energy consumption by using the forecasting method like [5][6][7][8][9][10]12] for China, [13] for Brazil, and [15] for South Africa. On the other hand, some studies used the grey Markov method with rolling mechanism and singular spectrum analysis for energy consumption forecasting like [16] for India.…”

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

“…Forecasting of energy consumption plays a key role of energy management ( Wei et al., 2019 ). Even since the last two years, grey prediction models have been widely used as a forecasting tool by many researchers in this field ( Li and Zhang, 2019 ; Zhang et al., 2019 ; Ye et al., 2019 ; Wang and Song, 2019 ; Wang et al., 2019 ; Lu, 2019 ; Wang et al., 2020 ; Ma and Wang, 2020 ).…”