Forecasting China’s Annual Biofuel Production Using an Improved Grey Model
Biofuel production in China suffers from many uncertainties due to concerns about the government’s support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel’s characteristics, we improve the predi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-10-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/8/10/12080 |
| _version_ | 1811298346604691456 |
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| author | Nana Geng Yong Zhang Yixiang Sun Yunjian Jiang Dandan Chen |
| author_facet | Nana Geng Yong Zhang Yixiang Sun Yunjian Jiang Dandan Chen |
| author_sort | Nana Geng |
| collection | DOAJ |
| description | Biofuel production in China suffers from many uncertainties due to concerns about the government’s support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel’s characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey–Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey–Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events. |
| first_indexed | 2024-04-13T06:18:07Z |
| format | Article |
| id | doaj.art-7da8758cbac94e498449c39782376d71 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T06:18:07Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7da8758cbac94e498449c39782376d712022-12-22T02:58:45ZengMDPI AGEnergies1996-10732015-10-01810120801209910.3390/en81012080en81012080Forecasting China’s Annual Biofuel Production Using an Improved Grey ModelNana Geng0Yong Zhang1Yixiang Sun2Yunjian Jiang3Dandan Chen4School of Transportation, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Transportation, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, ChinaSchool of Transportation, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Transportation, Southeast University, Nanjing 210096, Jiangsu, ChinaBiofuel production in China suffers from many uncertainties due to concerns about the government’s support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel’s characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey–Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey–Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.http://www.mdpi.com/1996-1073/8/10/12080grey modelMarkov modelfuzzy set theorycombination forecastbiofuel production |
| spellingShingle | Nana Geng Yong Zhang Yixiang Sun Yunjian Jiang Dandan Chen Forecasting China’s Annual Biofuel Production Using an Improved Grey Model Energies grey model Markov model fuzzy set theory combination forecast biofuel production |
| title | Forecasting China’s Annual Biofuel Production Using an Improved Grey Model |
| title_full | Forecasting China’s Annual Biofuel Production Using an Improved Grey Model |
| title_fullStr | Forecasting China’s Annual Biofuel Production Using an Improved Grey Model |
| title_full_unstemmed | Forecasting China’s Annual Biofuel Production Using an Improved Grey Model |
| title_short | Forecasting China’s Annual Biofuel Production Using an Improved Grey Model |
| title_sort | forecasting china s annual biofuel production using an improved grey model |
| topic | grey model Markov model fuzzy set theory combination forecast biofuel production |
| url | http://www.mdpi.com/1996-1073/8/10/12080 |
| work_keys_str_mv | AT nanageng forecastingchinasannualbiofuelproductionusinganimprovedgreymodel AT yongzhang forecastingchinasannualbiofuelproductionusinganimprovedgreymodel AT yixiangsun forecastingchinasannualbiofuelproductionusinganimprovedgreymodel AT yunjianjiang forecastingchinasannualbiofuelproductionusinganimprovedgreymodel AT dandanchen forecastingchinasannualbiofuelproductionusinganimprovedgreymodel |