China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions

Wang, Qiang; Li, Shuyu; Li, Rongrong

doi:10.1016/j.energy.2018.08.127

Cited by 80 publications

(36 citation statements)

References 24 publications

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“…The predicted values produced by these methods are the results of combining individual prediction results after weighting based on precision. Different from the traditional combinations, the approach used in this study is a combination of prediction steps [33]. Assume that the combined model includes two single models.…”

Section: The Autoregressive Integrated Moving Average Modelmentioning

confidence: 99%

“…The verification results confirm that the neural network model can be make accurate predictions in most cases. Wang et al [33] used the linear ARIMA to correct NMGM residuals to forecast China's dependency on foreign oil; they reported that China's dependency on foreign oil will exceed 80% of its energy expenditures by 2030. Hossain et al [34] used artificial neural network models to simultaneously predict new solar and wind energy and applies them to the climate of Queensland.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

2019

Sustainability

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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.

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Section: The Autoregressive Integrated Moving Average Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yang

2019

Sustainability

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“…Especially, studies have revealed that the LMDI approach showed a relatively satisfying performance when decomposing CO 2 emissions and energy consumption [15,[42][43][44]. In this paper, both additive LMDI and multiplicative LMDI methods from an extended Kaya identity [45][46][47][48][49] were adopted to give a more accurate revelation of the carbon emission changes brought from each effect. Unlike most research on decomposing electricity carbon dioxide emissions, we analyzed the electricity carbon emission from the perspective of the socioeconomic system, discussed the factors such as population and gross domestic product (GDP) per capita, and more importantly, considered the electricity sector characteristics.…”

Section: Decoupling Index From Multilevel Index Decompositionmentioning

confidence: 99%

Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China

Jiang

2018

Sustainability

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Carbon emissions from China’s electricity sector account for about one-seventh of the global carbon dioxide emissions, or half of China’s carbon dioxide emissions. A better understanding of the relationship between CO2 emissions and electric output would help develop and adjust carbon emission mitigation strategies for China’s electricity sector. Thus, we applied the electricity elasticity of carbon emissions to a decoupling index that we combined with advanced multilevel Logarithmic Mean Divisia Index tools in order to test the carbon emission response to the electric output and the main drivers. Then, we proposed a comparative decoupling stability analysis method. The results show that the electric output effect played the most significant role in increasing CO2 emissions from China’s electric sector. Also, “relative decoupling” was the main state during the study period (1991–2012). Moreover, the electricity elasticity of CO2 emissions had a better performance regarding stability in the analysis of China’s electricity output.

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“…Since the emergence of the issue about applying mathematical models to predict energy consumption, relevant researches have been carried out [1] and several related experiments have been implemented [2]. e recent study [3] has combined the nonlinear metabolic grey model (NMGM) and autoregressive integrated moving average (ARIMA) model and has used the linear ARIMA to correct NMGM forecasting residuals, which has improved forecasting accuracy steadily and given useful policy recommendation. e time-series forecasting techniques based on the metabolic grey model, autoregressive integrated moving average model-grey model, and induced ordered weighted geometric averaging operator have been investigated in [4], which has found a way to provide reliable information and has indicated that the results from the time-series and econometric forecasting technique are consistent.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions

Cited by 80 publications

References 24 publications

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

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

Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

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