A Novel Fractional Hausdorff Discrete Grey Model for Forecasting the Renewable Energy Consumption
Reducing carbon dioxide emissions and using renewable energy to replace fossil fuels have become an essential trend in future energy development. Renewable energy consumption has a significant impact on energy security so accurate prediction of renewable energy consumption can help the energy depart...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/8443619 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Reducing carbon dioxide emissions and using renewable energy to replace fossil fuels have become an essential trend in future energy development. Renewable energy consumption has a significant impact on energy security so accurate prediction of renewable energy consumption can help the energy department formulate relevant policies and adjust the energy structure. Based on this, a novel Fractional Hausdorff Discrete Grey Model, abbreviated FHDGM (1,1), is developed in this study. The paper investigates the model’s characteristics. The fractional-order r of the FHDGM (1,1) model is optimized using particle swarm optimization. Subsequently, through two empirical analyses, the prediction accuracy of the FHDGM (1,1) model is proven to be higher than that of other models. Finally, the proposed model is applied with a view to forecasting the consumption of renewable energy for the years 2021 to 2023 in three different areas: the Asia Pacific region, Europe, and the world. The study’s findings will offer crucial forecasting data for worldwide energy conservation and emission reduction initiatives. |
|---|---|
| ISSN: | 2314-4785 |