Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization
The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9200560 |
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| _version_ | 1832560501788246016 |
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| author | Xiaoshuang Luo Bo Zeng Hui Li Wenhao Zhou |
| author_facet | Xiaoshuang Luo Bo Zeng Hui Li Wenhao Zhou |
| author_sort | Xiaoshuang Luo |
| collection | DOAJ |
| description | The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments. |
| format | Article |
| id | doaj-art-0210d97ea9394667b61c2ab76f2ee657 |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-0210d97ea9394667b61c2ab76f2ee6572025-02-03T01:27:22ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/92005609200560Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination OptimizationXiaoshuang Luo0Bo Zeng1Hui Li2Wenhao Zhou3School of International Business, Chongqing Finance and Economics College, Chongqing 401320, ChinaSchool of Management Science and Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaSchool of Management Science and Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaCollege of Business Administration, Huaqiao University, Quanzhou, 362021, ChinaThe intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.http://dx.doi.org/10.1155/2021/9200560 |
| spellingShingle | Xiaoshuang Luo Bo Zeng Hui Li Wenhao Zhou Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization Journal of Mathematics |
| title | Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization |
| title_full | Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization |
| title_fullStr | Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization |
| title_full_unstemmed | Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization |
| title_short | Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization |
| title_sort | forecasting chinese wind power installed capacity using a novel grey model with parameters combination optimization |
| url | http://dx.doi.org/10.1155/2021/9200560 |
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