Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing
The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/24/8466 |
| _version_ | 1797505038193524736 |
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| author | Ragosebo Kgaugelo Modise Khumbulani Mpofu Olukorede Tijani Adenuga |
| author_facet | Ragosebo Kgaugelo Modise Khumbulani Mpofu Olukorede Tijani Adenuga |
| author_sort | Ragosebo Kgaugelo Modise |
| collection | DOAJ |
| description | The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation. |
| first_indexed | 2024-03-10T04:12:58Z |
| format | Article |
| id | doaj.art-46e6c06f5b9f44c0b0e4247cd5fd0193 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:12:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-46e6c06f5b9f44c0b0e4247cd5fd01932023-11-23T08:07:41ZengMDPI AGEnergies1996-10732021-12-011424846610.3390/en14248466Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport ManufacturingRagosebo Kgaugelo Modise0Khumbulani Mpofu1Olukorede Tijani Adenuga2Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDepartment of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaDepartment of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaThe long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation.https://www.mdpi.com/1996-1073/14/24/8466energy efficiencycarbon dioxide emissionenergy consumptionARIMA |
| spellingShingle | Ragosebo Kgaugelo Modise Khumbulani Mpofu Olukorede Tijani Adenuga Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing Energies energy efficiency carbon dioxide emission energy consumption ARIMA |
| title | Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing |
| title_full | Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing |
| title_fullStr | Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing |
| title_full_unstemmed | Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing |
| title_short | Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing |
| title_sort | energy and carbon emission efficiency prediction applications in future transport manufacturing |
| topic | energy efficiency carbon dioxide emission energy consumption ARIMA |
| url | https://www.mdpi.com/1996-1073/14/24/8466 |
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