scholarly journals 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

2018 ◽  
Vol 10 (7) ◽  
pp. 2552 ◽  
Author(s):  
Minglu Ma ◽  
Min Su ◽  
Shuyu Li ◽  
Feng Jiang ◽  
Rongrong Li
Keyword(s):  
South Africa ◽  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Model ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Non Linear ◽  
Moving Average Model

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

scholarly journals Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

2019 ◽  
Vol 11 (3) ◽  
pp. 695 ◽  
Author(s):  
Shuyu Li ◽  
Xuan Yang ◽  
Rongrong Li
Keyword(s):  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Annual Rate ◽  
Average Model ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Combined Models ◽  
Relative Errors

India’s coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India’s coal consumption during 1995–2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018–2030.

scholarly journals Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2574 ◽  
Author(s):  
Yeqi An ◽  
Yulin Zhou ◽  
Rongrong Li
Keyword(s):  
Power Generation ◽  
Electricity Generation ◽  
Moving Average ◽  
Probabilistic Methods ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

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

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 10 ◽  
Author(s):  
Minglu Ma ◽  
Zhuangzhuang Wang
Keyword(s):  
South Africa ◽  
Energy Consumption ◽  
High Reliability ◽  
Moving Average ◽  
Supply And Demand ◽  
Arima Model ◽  
Research Result ◽  
Grey Model ◽  
African Countries ◽  
Autoregressive Integrated Moving Average

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.

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

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3278 ◽  
Author(s):  
Xinyu Han ◽  
Rongrong Li
Keyword(s):  
Energy Consumption ◽  
East Africa ◽  
Moving Average ◽  
Arima Model ◽  
Grey Model ◽  
Systematic Research ◽  
Average Model ◽  
Non Linear ◽  
Moving Average Model ◽  
Auto Regressive

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.

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