Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management
Accurately predicting industrial electricity consumption is essential for optimizing energy efficiency, and reducing costs in industrial operations. This study presents a novel hybrid prediction model based on radial basis function neural network (RBFNN) and kernelized support vector regression (KSV...
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| Format: | Article |
| Language: | English |
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Iranian Association for Energy Economics
2024-10-01
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| Series: | Environmental Energy and Economic Research |
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| Online Access: | https://www.eeer.ir/article_208350_5b0e8da8ec760cdee7410496cb3bcfe4.pdf |
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| author | Mohsen Rezaei |
| author_facet | Mohsen Rezaei |
| author_sort | Mohsen Rezaei |
| collection | DOAJ |
| description | Accurately predicting industrial electricity consumption is essential for optimizing energy efficiency, and reducing costs in industrial operations. This study presents a novel hybrid prediction model based on radial basis function neural network (RBFNN) and kernelized support vector regression (KSVR) methods (RBFNN-KSVR) for estimating industrial electricity consumption. Key input variables include population, electricity price in the industry sector, gross domestic product (GDP), and the number of electricity subscribers. The proposed hybrid model was implemented in a real-world case study to estimate industrial electricity consumption in Iran and compared against base methods (RBFNN, SVR, and KSVR). Extensive evaluation reveals the superior performance of the RBFNN-KSVR model in predicting industrial electricity consumption. This study provides a robust and reliable approach for industrial stakeholders to enhance energy planning, identify energy-saving opportunities, and ensure a stable power supply. The findings have significant implications for optimizing energy usage, improving efficiency, and reducing costs in industrial operations. |
| format | Article |
| id | doaj-art-72627f66bc7246e296e65e23e26a32ec |
| institution | Kabale University |
| issn | 2538-4988 2676-4997 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Iranian Association for Energy Economics |
| record_format | Article |
| series | Environmental Energy and Economic Research |
| spelling | doaj-art-72627f66bc7246e296e65e23e26a32ec2025-08-20T03:49:55ZengIranian Association for Energy EconomicsEnvironmental Energy and Economic Research2538-49882676-49972024-10-018410.22097/eeer.2024.463837.1331208350Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy ManagementMohsen Rezaei0Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, IranAccurately predicting industrial electricity consumption is essential for optimizing energy efficiency, and reducing costs in industrial operations. This study presents a novel hybrid prediction model based on radial basis function neural network (RBFNN) and kernelized support vector regression (KSVR) methods (RBFNN-KSVR) for estimating industrial electricity consumption. Key input variables include population, electricity price in the industry sector, gross domestic product (GDP), and the number of electricity subscribers. The proposed hybrid model was implemented in a real-world case study to estimate industrial electricity consumption in Iran and compared against base methods (RBFNN, SVR, and KSVR). Extensive evaluation reveals the superior performance of the RBFNN-KSVR model in predicting industrial electricity consumption. This study provides a robust and reliable approach for industrial stakeholders to enhance energy planning, identify energy-saving opportunities, and ensure a stable power supply. The findings have significant implications for optimizing energy usage, improving efficiency, and reducing costs in industrial operations.https://www.eeer.ir/article_208350_5b0e8da8ec760cdee7410496cb3bcfe4.pdfelectricity consumptionhybrid prediction modelradial basis function neural networkkernelized support vector regressionrbfnn-ksvr |
| spellingShingle | Mohsen Rezaei Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management Environmental Energy and Economic Research electricity consumption hybrid prediction model radial basis function neural network kernelized support vector regression rbfnn-ksvr |
| title | Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management |
| title_full | Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management |
| title_fullStr | Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management |
| title_full_unstemmed | Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management |
| title_short | Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management |
| title_sort | forecasting industrial electricity consumption in iran a novel hybrid approach for sustainable energy management |
| topic | electricity consumption hybrid prediction model radial basis function neural network kernelized support vector regression rbfnn-ksvr |
| url | https://www.eeer.ir/article_208350_5b0e8da8ec760cdee7410496cb3bcfe4.pdf |
| work_keys_str_mv | AT mohsenrezaei forecastingindustrialelectricityconsumptioninirananovelhybridapproachforsustainableenergymanagement |