Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm
Effectively managing and optimizing energy resources to accommodate population growth while minimizing carbon emissions has become increasingly intricate. A proficient approach to this dilemma is accurately predicting energy usage and emissions across diverse sectors. This paper unveils a genetic al...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025001458 |
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| author | Md. Sadikul Hasan Md. Tarequzzaman Md. Moznuzzaman Md Abdul Ahad Juel |
| author_facet | Md. Sadikul Hasan Md. Tarequzzaman Md. Moznuzzaman Md Abdul Ahad Juel |
| author_sort | Md. Sadikul Hasan |
| collection | DOAJ |
| description | Effectively managing and optimizing energy resources to accommodate population growth while minimizing carbon emissions has become increasingly intricate. A proficient approach to this dilemma is accurately predicting energy usage and emissions across diverse sectors. This paper unveils a genetic algorithm (GA)-optimized support vector regression (SVR) model designed to (i) predict electricity generation, (ii) predict energy consumption in four primary sectors—residential, industrial, commercial, and agricultural, and (iii) estimate sector-specific carbon emissions. The proposed model's efficacy is assessed by calculating the R2 value, mean absolute error (MAE), root mean squared error (RMSE), and residual plot. The model achieved high accuracy in predicting energy generation, with an MAE of 1.18 %, and yielded reliable sectoral consumption predictions, reflected in MAE values of 1.22 % (residential), 4.98 % (industrial), 4.40 % (commercial), and 4.04 % (agricultural). The residuals exhibited homoscedasticity, and the R2 value approached one. The model predicts that by 2027, the residential sector will consume 55748.66 GWh of energy, the commercial sector 14892.49 GWh, the industrial sector 32642.35 GWh, and the agricultural sector 2288.37 GWh. It has been predicted that by 2027, these four sectors will release 75437.96-billion-gram carbon equivalents. |
| format | Article |
| id | doaj-art-d94679f41dd74d559825fe86bb359b4f |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-d94679f41dd74d559825fe86bb359b4f2025-02-02T05:28:07ZengElsevierHeliyon2405-84402025-01-01112e41765Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithmMd. Sadikul Hasan0Md. Tarequzzaman1Md. Moznuzzaman2Md Abdul Ahad Juel3Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore-7408, BangladeshDepartment of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore-7408, Bangladesh; Corresponding author.Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore-7408, BangladeshDepartment of Electrical and Electronic Engineering, Bangladesh University of Business and Technology, Dhaka-1216, BangladeshEffectively managing and optimizing energy resources to accommodate population growth while minimizing carbon emissions has become increasingly intricate. A proficient approach to this dilemma is accurately predicting energy usage and emissions across diverse sectors. This paper unveils a genetic algorithm (GA)-optimized support vector regression (SVR) model designed to (i) predict electricity generation, (ii) predict energy consumption in four primary sectors—residential, industrial, commercial, and agricultural, and (iii) estimate sector-specific carbon emissions. The proposed model's efficacy is assessed by calculating the R2 value, mean absolute error (MAE), root mean squared error (RMSE), and residual plot. The model achieved high accuracy in predicting energy generation, with an MAE of 1.18 %, and yielded reliable sectoral consumption predictions, reflected in MAE values of 1.22 % (residential), 4.98 % (industrial), 4.40 % (commercial), and 4.04 % (agricultural). The residuals exhibited homoscedasticity, and the R2 value approached one. The model predicts that by 2027, the residential sector will consume 55748.66 GWh of energy, the commercial sector 14892.49 GWh, the industrial sector 32642.35 GWh, and the agricultural sector 2288.37 GWh. It has been predicted that by 2027, these four sectors will release 75437.96-billion-gram carbon equivalents.http://www.sciencedirect.com/science/article/pii/S2405844025001458SVRCarbon emissionsPredictionEnergy consumptionEnergy management |
| spellingShingle | Md. Sadikul Hasan Md. Tarequzzaman Md. Moznuzzaman Md Abdul Ahad Juel Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm Heliyon SVR Carbon emissions Prediction Energy consumption Energy management |
| title | Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| title_full | Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| title_fullStr | Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| title_full_unstemmed | Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| title_short | Prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| title_sort | prediction of energy consumption in four sectors using support vector regression optimized with genetic algorithm |
| topic | SVR Carbon emissions Prediction Energy consumption Energy management |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025001458 |
| work_keys_str_mv | AT mdsadikulhasan predictionofenergyconsumptioninfoursectorsusingsupportvectorregressionoptimizedwithgeneticalgorithm AT mdtarequzzaman predictionofenergyconsumptioninfoursectorsusingsupportvectorregressionoptimizedwithgeneticalgorithm AT mdmoznuzzaman predictionofenergyconsumptioninfoursectorsusingsupportvectorregressionoptimizedwithgeneticalgorithm AT mdabdulahadjuel predictionofenergyconsumptioninfoursectorsusingsupportvectorregressionoptimizedwithgeneticalgorithm |