Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption
Currently, coal is still the main energy source for China’s economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic devel...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2023/8606977 |
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| _version_ | 1832547365122211840 |
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| author | Guodang Zhao Xuyang Guo Xin Wang Dezhi Zheng |
| author_facet | Guodang Zhao Xuyang Guo Xin Wang Dezhi Zheng |
| author_sort | Guodang Zhao |
| collection | DOAJ |
| description | Currently, coal is still the main energy source for China’s economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China’s coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China’s coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China’s coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China’s coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies. |
| format | Article |
| id | doaj-art-720d37d551b44cc29386770df4e773d5 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-720d37d551b44cc29386770df4e773d52025-02-03T06:45:10ZengWileyDiscrete Dynamics in Nature and Society1607-887X2023-01-01202310.1155/2023/8606977Using a Novel Fractal-Time-Series Prediction Model to Predict Coal ConsumptionGuodang Zhao0Xuyang Guo1Xin Wang2Dezhi Zheng3Business SchoolBusiness SchoolKewen CollegeChina Coal Research InstituteCurrently, coal is still the main energy source for China’s economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China’s coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China’s coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China’s coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China’s coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies.http://dx.doi.org/10.1155/2023/8606977 |
| spellingShingle | Guodang Zhao Xuyang Guo Xin Wang Dezhi Zheng Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption Discrete Dynamics in Nature and Society |
| title | Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption |
| title_full | Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption |
| title_fullStr | Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption |
| title_full_unstemmed | Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption |
| title_short | Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption |
| title_sort | using a novel fractal time series prediction model to predict coal consumption |
| url | http://dx.doi.org/10.1155/2023/8606977 |
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