A Novel Grey Seasonal Model for Natural Gas Production Forecasting
To accurately predict the time series of energy data, an optimized Hausdorff fractional grey seasonal model was proposed based on the complex characteristics of seasonal fluctuations and local random oscillations of seasonal energy data. This paper used a new seasonal index to eliminate the seasonal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Fractal and Fractional |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3110/7/6/422 |
| _version_ | 1797594746675265536 |
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| author | Yuzhen Chen Hui Wang Suzhen Li Rui Dong |
| author_facet | Yuzhen Chen Hui Wang Suzhen Li Rui Dong |
| author_sort | Yuzhen Chen |
| collection | DOAJ |
| description | To accurately predict the time series of energy data, an optimized Hausdorff fractional grey seasonal model was proposed based on the complex characteristics of seasonal fluctuations and local random oscillations of seasonal energy data. This paper used a new seasonal index to eliminate the seasonal variation of the data and weaken the local random fluctuations. Furthermore, the Hausdorff fractional accumulation operator was introduced into the traditional grey prediction model to improve the weight of new information, and the particle swarm optimization algorithm was used to find the nonlinear parameters of the model. In order to verify the reliability of the new model in energy forecasting, the new model was applied to two different energy types, hydropower and wind power. The experimental results indicated that the model can effectively predict quarterly time series of energy data. Based on this, we used China’s quarterly natural gas production data from 2015 to 2021 as samples to forecast those for 2022–2024. In addition, we also compared the proposed model with the traditional statistical models and the grey seasonal models. The comparison results showed that the new model had obvious advantages in predicting quarterly data of natural gas production, and the accurate prediction results can provide a reference for natural gas resource allocation. |
| first_indexed | 2024-03-11T02:27:52Z |
| format | Article |
| id | doaj.art-a3300a5fe17047d991bcf153bd91c555 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-11T02:27:52Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-a3300a5fe17047d991bcf153bd91c5552023-11-18T10:29:05ZengMDPI AGFractal and Fractional2504-31102023-05-017642210.3390/fractalfract7060422A Novel Grey Seasonal Model for Natural Gas Production ForecastingYuzhen Chen0Hui Wang1Suzhen Li2Rui Dong3School of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang 453003, ChinaTo accurately predict the time series of energy data, an optimized Hausdorff fractional grey seasonal model was proposed based on the complex characteristics of seasonal fluctuations and local random oscillations of seasonal energy data. This paper used a new seasonal index to eliminate the seasonal variation of the data and weaken the local random fluctuations. Furthermore, the Hausdorff fractional accumulation operator was introduced into the traditional grey prediction model to improve the weight of new information, and the particle swarm optimization algorithm was used to find the nonlinear parameters of the model. In order to verify the reliability of the new model in energy forecasting, the new model was applied to two different energy types, hydropower and wind power. The experimental results indicated that the model can effectively predict quarterly time series of energy data. Based on this, we used China’s quarterly natural gas production data from 2015 to 2021 as samples to forecast those for 2022–2024. In addition, we also compared the proposed model with the traditional statistical models and the grey seasonal models. The comparison results showed that the new model had obvious advantages in predicting quarterly data of natural gas production, and the accurate prediction results can provide a reference for natural gas resource allocation.https://www.mdpi.com/2504-3110/7/6/422energy productionHausdorff fractional order accumulationgrey modelparticle swarm optimization algorithmseasonal index |
| spellingShingle | Yuzhen Chen Hui Wang Suzhen Li Rui Dong A Novel Grey Seasonal Model for Natural Gas Production Forecasting Fractal and Fractional energy production Hausdorff fractional order accumulation grey model particle swarm optimization algorithm seasonal index |
| title | A Novel Grey Seasonal Model for Natural Gas Production Forecasting |
| title_full | A Novel Grey Seasonal Model for Natural Gas Production Forecasting |
| title_fullStr | A Novel Grey Seasonal Model for Natural Gas Production Forecasting |
| title_full_unstemmed | A Novel Grey Seasonal Model for Natural Gas Production Forecasting |
| title_short | A Novel Grey Seasonal Model for Natural Gas Production Forecasting |
| title_sort | novel grey seasonal model for natural gas production forecasting |
| topic | energy production Hausdorff fractional order accumulation grey model particle swarm optimization algorithm seasonal index |
| url | https://www.mdpi.com/2504-3110/7/6/422 |
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