Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang, Feng; Yang, Xue; Li, Shuyu

doi:10.3390/su10072225

Cited by 30 publications

(12 citation statements)

References 40 publications

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“…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.…”

Section: Conclusion and Discussionmentioning

confidence: 89%

“…In contrast, GHGs will be reduced when renewable energy consumption increases. In India, Jiang, Yang and Li [41] used metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) in their work to estimate energy demand. The study indicates a 5% growth in energy consumption from 2017 to 2030.…”

Section: Literature Reviewmentioning

confidence: 99%

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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

Sutthichaimethee

Chatchorfa

Suyaprom

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

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Section: Conclusion and Discussionmentioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

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

Sutthichaimethee

Chatchorfa

Suyaprom

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…Deng [14] built the grey system theory-GM (1,1) which has been a typical forecasting model, and widely used in energy [15,16]. A large number of scholars have carried out further research on GM (1,1) model in recent years [17,18].…”

Section: The Development Of Grey Theory Modelmentioning

confidence: 99%

Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

Pei

2019

Sustainability

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The new energy vehicles (NEVs) industry has been regarded as the primary industry involving in the transformation of the China automobile industry and environmental pollution control. Based on the quarterly fluctuation characteristics of NEVs’ sales volume in China, this research puts forwards a data grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM (1,1)). The main ideas of this work are to effectively predict quarterly fluctuation of NEVs industry by introducing a data grouping approach into the NGBM (1,1) model, and then use the particle swarm optimization (PSO) algorithm to optimize the parameters of the model so as to increase forecasting precision. By empirical comparison between the DGA-based NGBM (1,1) and existing data grouping approach-based GM (1,1) model (DGA-based GM (1,1)), DGA-based NGBM (1,1) can effectively reduce the prediction error resulting from quarterly fluctuation of sales volume of the NEVs, and prediction performance are proven to be favorable. The results of out-of-sample forecasting using the model proposed show that the sales volume of NEVs in China will increase by 57% in 2019–2020 with a quarterly fluctuation. In 2020, the sales volume of NEVs will exceeds the target of 2 million in the “13th Five-Year Strategic Development Plan”. Therefore, China needs to pay more attention to infrastructure construction and after-sales service for NEVs.

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“…Last but not least, Jiang, Yang and Li [34] carried out a comparative study of forecasting an energy demand in India by deploying various methods, namely MGM, ARIMA, MGM-ARIMA, and back propagation neural network (BP). Based on their predicted result, India's energy demand will potentially increase by 4.75% from 2017 to 2030.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Sutthichaimethee

Kubaha

2018

Sustainability

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The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO 2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO 2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand's construction sector by applying a model incorporating the longand short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO 2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study's findings reveal that the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO 2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO 2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO 2 Eq. in 2038. Also, the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

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Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Cited by 30 publications

References 40 publications

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

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

Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

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