A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network
The dynamic load forecasting of scraper conveyer is one of the key problems that need to be solved in unmanned coal mining. The dynamic load forecasting system of scraper conveyer is a complex, multivariable, and nonlinear system, and there are coupling relations between every variable. It is very d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721009392 |
| _version_ | 1818435887279636480 |
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| author | Haitao He Zhengxiong Lu Chuanwei Zhang Yuan Wang Wei Guo Shuanfeng Zhao |
| author_facet | Haitao He Zhengxiong Lu Chuanwei Zhang Yuan Wang Wei Guo Shuanfeng Zhao |
| author_sort | Haitao He |
| collection | DOAJ |
| description | The dynamic load forecasting of scraper conveyer is one of the key problems that need to be solved in unmanned coal mining. The dynamic load forecasting system of scraper conveyer is a complex, multivariable, and nonlinear system, and there are coupling relations between every variable. It is very difficult to establish precise mathematic model. Therefore, based on rough set and the gated recurrent units (GRU), this study proposes a data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing GRU network. First, the rough set was applied to carry on for a variety of factors affecting load forecasting of scraper conveyer to optimize the model input, and the importance of each attribute for load of scraper conveyer was obtained. Then, a multilayered self-normalizing gated recurrent units (MS-GRU) model is proposed for the dynamic load forecasting of scraper conveyer. This model introduces scaled exponential linear units (SELU) activation function to squash the hidden states to calculate the output of the model, and the exploding and vanishing gradient problem are overcome in a stacked GRU neural network. Finally, an experiment is applied to verify the proposed model in this paper. The experimental results show that this article Compared with the existing methods, the model shows a higher accuracy rate 95.8%, which can well complete the prediction of the operating parameters of the shearer. |
| first_indexed | 2024-12-14T17:00:01Z |
| format | Article |
| id | doaj.art-a797f5974d34478d8d7d39fd5e4499d1 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-14T17:00:01Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-a797f5974d34478d8d7d39fd5e4499d12022-12-21T22:53:52ZengElsevierEnergy Reports2352-48472021-11-01713521362A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent networkHaitao He0Zhengxiong Lu1Chuanwei Zhang2Yuan Wang3Wei Guo4Shuanfeng Zhao5School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaSchool of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaCorresponding author.; School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, ChinaThe dynamic load forecasting of scraper conveyer is one of the key problems that need to be solved in unmanned coal mining. The dynamic load forecasting system of scraper conveyer is a complex, multivariable, and nonlinear system, and there are coupling relations between every variable. It is very difficult to establish precise mathematic model. Therefore, based on rough set and the gated recurrent units (GRU), this study proposes a data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing GRU network. First, the rough set was applied to carry on for a variety of factors affecting load forecasting of scraper conveyer to optimize the model input, and the importance of each attribute for load of scraper conveyer was obtained. Then, a multilayered self-normalizing gated recurrent units (MS-GRU) model is proposed for the dynamic load forecasting of scraper conveyer. This model introduces scaled exponential linear units (SELU) activation function to squash the hidden states to calculate the output of the model, and the exploding and vanishing gradient problem are overcome in a stacked GRU neural network. Finally, an experiment is applied to verify the proposed model in this paper. The experimental results show that this article Compared with the existing methods, the model shows a higher accuracy rate 95.8%, which can well complete the prediction of the operating parameters of the shearer.http://www.sciencedirect.com/science/article/pii/S2352484721009392Scraper conveyerRough setMultilayered self-normalizing GRULoad forecasting |
| spellingShingle | Haitao He Zhengxiong Lu Chuanwei Zhang Yuan Wang Wei Guo Shuanfeng Zhao A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network Energy Reports Scraper conveyer Rough set Multilayered self-normalizing GRU Load forecasting |
| title | A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network |
| title_full | A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network |
| title_fullStr | A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network |
| title_full_unstemmed | A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network |
| title_short | A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network |
| title_sort | data driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self normalizing gated recurrent network |
| topic | Scraper conveyer Rough set Multilayered self-normalizing GRU Load forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721009392 |
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