The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data
With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (M...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10384333/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540508130377728 |
|---|---|
| author | Ji Luo Wuyang Zhuo Siyan Liu Bingfei Xu |
| author_facet | Ji Luo Wuyang Zhuo Siyan Liu Bingfei Xu |
| author_sort | Ji Luo |
| collection | DOAJ |
| description | With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance. |
| format | Article |
| id | doaj-art-0f78391df8564486ba7359da51b0cca5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0f78391df8564486ba7359da51b0cca52025-02-05T00:00:42ZengIEEEIEEE Access2169-35362024-01-0112146901470210.1109/ACCESS.2024.335146810384333The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big DataJi Luo0Wuyang Zhuo1https://orcid.org/0009-0007-5463-6302Siyan Liu2Bingfei Xu3School of Economics and Management, Shanghai Polytechnic University, Shanghai, ChinaSchool of Economics and Management, Shanghai Polytechnic University, Shanghai, ChinaGuiyang Central Sub-Branch, People's Bank of China, Guiyang, Guizhou, ChinaBeijing Branch, Postal Saving Bank of China, Beijing, ChinaWith the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance.https://ieeexplore.ieee.org/document/10384333/Low-carbon energyMUQHS-DMFSE composite modellow-carbon economyenergy demandresource supply |
| spellingShingle | Ji Luo Wuyang Zhuo Siyan Liu Bingfei Xu The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data IEEE Access Low-carbon energy MUQHS-DMFSE composite model low-carbon economy energy demand resource supply |
| title | The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data |
| title_full | The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data |
| title_fullStr | The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data |
| title_full_unstemmed | The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data |
| title_short | The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data |
| title_sort | optimization of carbon emission prediction in low carbon energy economy under big data |
| topic | Low-carbon energy MUQHS-DMFSE composite model low-carbon economy energy demand resource supply |
| url | https://ieeexplore.ieee.org/document/10384333/ |
| work_keys_str_mv | AT jiluo theoptimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT wuyangzhuo theoptimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT siyanliu theoptimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT bingfeixu theoptimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT jiluo optimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT wuyangzhuo optimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT siyanliu optimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata AT bingfeixu optimizationofcarbonemissionpredictioninlowcarbonenergyeconomyunderbigdata |