Coal consumption forecasting using an optimized grey model: The case of the world's top three coal consumers

Abstract Carbon emissions from fossil energy not only cause a lot of extreme weathers, but also global warming. Accurately forecasting of electricity demand can promote the development of the renewable energy, which is vital to achieving the goal of carbon peak and carbon neutrality. In this paper, a nonlinear interval grey model based on genetic algorithm and BP neural network optimization (BPGA-IGM (1,1)) is proposed to predict electricity consumption. Firstly, based on the forecast of China's energy consumption and China's coal consumption, the reliability and superiority of the BPGA-IGM (1,1) model have been verified. Then, the model and other competing models are applied to forecast Shanghai's electricity consumption. The empirical results show that the model designed in this paper could obtain more accurate and reliable prediction results. According to the empirical results, Shanghai's electricity consumption continues to rise to a higher level of no less than 1978.19 million kWh by 2025. On the basis of this issue, several suggestions have been offered.

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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

Sustainability ◽

10.3390/su10072552 ◽

2018 ◽

Vol 10 (7) ◽

pp. 2552 ◽

Cited By ~ 4

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.

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A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China

Energy ◽

10.1016/j.energy.2019.04.096 ◽

2019 ◽

Vol 178 ◽

pp. 487-507 ◽

Cited By ~ 78

Author(s):

Xin Ma ◽

Xie Mei ◽

Wenqing Wu ◽

Xinxing Wu ◽

Bo Zeng

Keyword(s):

Natural Gas ◽

Grey Model ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Coal Consumption

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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

Sustainability ◽

10.3390/su11030695 ◽

2019 ◽

Vol 11 (3) ◽

pp. 695 ◽

Cited By ~ 3

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.

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Coal Consumption of Locomotives

Scientific American ◽

10.1038/scientificamerican11121904-24137asupp ◽

1904 ◽

Vol 58 (1506supp) ◽

pp. 24137-24137

Keyword(s):

Coal Consumption

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Coal's Persistence in the Capitalist World-Economy

Sociology of Development ◽

10.1525/sod.2020.6.2.194 ◽

2020 ◽

Vol 6 (2) ◽

pp. 194-221 ◽

Cited By ~ 1

Author(s):

Paul K. Gellert ◽

Paul S. Ciccantell

Keyword(s):

World Economy ◽

Industrial Revolution ◽

Energy Transition ◽

Ecological Modernization ◽

World Systems ◽

Coal Consumption ◽

The World ◽

Capitalist World Economy ◽

Capitalist World

Predominant analyses of energy offer insufficient theoretical and political-economic insight into the persistence of coal and other fossil fuels. The dominant narrative of coal powering the Industrial Revolution, and Great Britain's world dominance in the nineteenth century giving way to a U.S.- and oil-dominated twentieth century, is marred by teleological assumptions. The key assumption that a complete energy “transition” will occur leads some to conceive of a renewable-energy-dominated twenty-first century led by China. After critiquing the teleological assumptions of modernization, ecological modernization, energetics, and even world-systems analysis of energy “transition,” this paper offers a world-systems perspective on the “raw” materialism of coal. Examining the material characteristics of coal and the unequal structure of the world-economy, the paper uses long-term data from governmental and private sources to reveal the lack of transition as new sources of energy are added. The increases in coal consumption in China and India as they have ascended in the capitalist world-economy have more than offset the leveling-off and decline in some core nations. A true global peak and decline (let alone full substitution) in energy generally and coal specifically has never happened. The future need not repeat the past, but technical, policy, and movement approaches will not get far without addressing the structural imperatives of capitalist growth and the uneven power structures and processes of long-term change of the world-system.

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Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

Journal of Business and Economics ◽

10.15341/jbe(2155-7950)/09.10.2019/006 ◽

2019 ◽

Vol 10 (9) ◽

pp. 852-860

Author(s):

Mahmoud Elsayed ◽

◽

Amr Soliman ◽

Keyword(s):

System Theory ◽

Grey System Theory ◽

Insurance Market ◽

Grey Model ◽

Loss Ratio ◽

Grey System ◽

Mathematical Technique ◽

Supervisory Authority ◽

Error Ratio ◽

Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

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