Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms
Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable e...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2322509 |
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| _version_ | 1846136024794660864 |
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| author | Hossein Moayedi Azfarizal Mukhtar Nidhal Ben Khedher Isam Elbadawi Mouldi Ben Amara Quynh TT Nima Khalilpoor |
| author_facet | Hossein Moayedi Azfarizal Mukhtar Nidhal Ben Khedher Isam Elbadawi Mouldi Ben Amara Quynh TT Nima Khalilpoor |
| author_sort | Hossein Moayedi |
| collection | DOAJ |
| description | Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set. |
| format | Article |
| id | doaj-art-784b59f87f664b2193f13b2e7e1b26b3 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-784b59f87f664b2193f13b2e7e1b26b32024-12-09T09:43:46ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2322509Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithmsHossein Moayedi0Azfarizal Mukhtar1Nidhal Ben Khedher2Isam Elbadawi3Mouldi Ben Amara4Quynh TT5Nima Khalilpoor6Institute of Research and Development, Duy Tan University, Da Nang, VietnamInstitute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, Kajang, MalaysiaDepartment of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, University of Ha'il, Ha'il, Saudi ArabiaApplied college, University of Ha'il, Ha'il, Saudi ArabiaInstitute of Research and Development, Duy Tan University, Da Nang, VietnamDepartment of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, IranEnergy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.https://www.tandfonline.com/doi/10.1080/19942060.2024.2322509EnergyCO2 emissionsartificial intelligentnature-inspired algorithms |
| spellingShingle | Hossein Moayedi Azfarizal Mukhtar Nidhal Ben Khedher Isam Elbadawi Mouldi Ben Amara Quynh TT Nima Khalilpoor Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms Engineering Applications of Computational Fluid Mechanics Energy CO2 emissions artificial intelligent nature-inspired algorithms |
| title | Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms |
| title_full | Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms |
| title_fullStr | Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms |
| title_full_unstemmed | Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms |
| title_short | Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms |
| title_sort | forecasting of energy related carbon dioxide emission using ann combined with hybrid metaheuristic optimization algorithms |
| topic | Energy CO2 emissions artificial intelligent nature-inspired algorithms |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2322509 |
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