BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand
The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this researc...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-08-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3373-en.pdf |
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| author | Somayeh Talebzadeh Reza Radfar Abbas Toloei Ashlaghi |
| author_facet | Somayeh Talebzadeh Reza Radfar Abbas Toloei Ashlaghi |
| author_sort | Somayeh Talebzadeh |
| collection | DOAJ |
| description | The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques. |
| format | Article |
| id | doaj-art-e8ebf6476af54226bb151c9fcdbc1de2 |
| institution | DOAJ |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-e8ebf6476af54226bb151c9fcdbc1de22025-08-20T03:20:39ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-08-0121333733373BIMLP Model Based on Deep Learning for Predicting Electrical Load DemandSomayeh Talebzadeh0Reza Radfar1Abbas Toloei Ashlaghi2 Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.http://ijeee.iust.ac.ir/article-1-3373-en.pdfmlpbaggingregressionelectrical load demand |
| spellingShingle | Somayeh Talebzadeh Reza Radfar Abbas Toloei Ashlaghi BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand Iranian Journal of Electrical and Electronic Engineering mlp bagging regression electrical load demand |
| title | BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand |
| title_full | BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand |
| title_fullStr | BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand |
| title_full_unstemmed | BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand |
| title_short | BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand |
| title_sort | bimlp model based on deep learning for predicting electrical load demand |
| topic | mlp bagging regression electrical load demand |
| url | http://ijeee.iust.ac.ir/article-1-3373-en.pdf |
| work_keys_str_mv | AT somayehtalebzadeh bimlpmodelbasedondeeplearningforpredictingelectricalloaddemand AT rezaradfar bimlpmodelbasedondeeplearningforpredictingelectricalloaddemand AT abbastoloeiashlaghi bimlpmodelbasedondeeplearningforpredictingelectricalloaddemand |