MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy
The rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective re...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ad9086 |
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| _version_ | 1846151266611232768 |
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| author | Entao Luo Li Shi Jiyan Liu Zheng Wu Guoyun Duan Lingxuan Zeng Tangsen Huang |
| author_facet | Entao Luo Li Shi Jiyan Liu Zheng Wu Guoyun Duan Lingxuan Zeng Tangsen Huang |
| author_sort | Entao Luo |
| collection | DOAJ |
| description | The rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. This model comprehensively considers the impact of various carbon emission-related factors across different regions in China. By employing one-hot encoding, we address the difficulties traditional classifiers face when handling categorical data, enabling predictions under more realistic conditions. To optimize the nonlinear characteristics of the data and overcome the limitations of grey models, we utilize dynamic iterative time series training to address errors due to data inaccuracies effectively. Experimental results demonstrate that our approach outperforms other methods in filtering out irrelevant data, achieving an average relative residual of 0.055 and an average ratio bias of 0.049, translating to more precise prediction accuracy. |
| format | Article |
| id | doaj-art-9bc4f6997218412bbe807c9726c7f6b0 |
| institution | Kabale University |
| issn | 2515-7620 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-9bc4f6997218412bbe807c9726c7f6b02024-11-27T17:14:59ZengIOP PublishingEnvironmental Research Communications2515-76202024-01-0161111503410.1088/2515-7620/ad9086MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracyEntao Luo0https://orcid.org/0000-0002-7925-2272Li Shi1Jiyan Liu2Zheng Wu3Guoyun Duan4Lingxuan Zeng5Tangsen Huang6The School of Information Engineering, Hunan University of Science and Engineering , Yongzhou, 425199, People's Republic of ChinaThe School of Information Engineering, Hunan University of Science and Engineering , Yongzhou, 425199, People's Republic of ChinaThe School of Computer Science, Hunan University of Technology and Business , Changsha, 410083, People's Republic of ChinaThe School of Computer Science and Engineering, Central South University , Changsha, 410083, People's Republic of ChinaThe College of Computer Science and Electronic Engineering, Hunan University , Changsha, 410082, People's Republic of ChinaThe School of Information Engineering, Hunan University of Science and Engineering , Yongzhou, 425199, People's Republic of ChinaThe School of Information Engineering, Hunan University of Science and Engineering , Yongzhou, 425199, People's Republic of ChinaThe rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. This model comprehensively considers the impact of various carbon emission-related factors across different regions in China. By employing one-hot encoding, we address the difficulties traditional classifiers face when handling categorical data, enabling predictions under more realistic conditions. To optimize the nonlinear characteristics of the data and overcome the limitations of grey models, we utilize dynamic iterative time series training to address errors due to data inaccuracies effectively. Experimental results demonstrate that our approach outperforms other methods in filtering out irrelevant data, achieving an average relative residual of 0.055 and an average ratio bias of 0.049, translating to more precise prediction accuracy.https://doi.org/10.1088/2515-7620/ad9086gray modelneural networkbackpropagationmetabolismdata prediction |
| spellingShingle | Entao Luo Li Shi Jiyan Liu Zheng Wu Guoyun Duan Lingxuan Zeng Tangsen Huang MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy Environmental Research Communications gray model neural network backpropagation metabolism data prediction |
| title | MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy |
| title_full | MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy |
| title_fullStr | MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy |
| title_full_unstemmed | MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy |
| title_short | MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy |
| title_sort | mnpm research on metabolic neural network prediction model for predicting carbon emission accuracy |
| topic | gray model neural network backpropagation metabolism data prediction |
| url | https://doi.org/10.1088/2515-7620/ad9086 |
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