Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm
The paper discusses a technique for detecting electrical fires in residential buildings using the Gradient Boosting Machine (GBM) algorithm. The features of the algorithm process data related to current, voltage, and total harmonic distortion (THD) from electrical systems, considering resistive load...
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| Format: | Article |
| Language: | English |
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Erbil Polytechnic University
2025-06-01
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| Series: | Polytechnic Journal |
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| Online Access: | https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/7 |
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| _version_ | 1849329432599199744 |
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| author | Sharaban Taha Ahmed Fadhil Aula |
| author_facet | Sharaban Taha Ahmed Fadhil Aula |
| author_sort | Sharaban Taha Ahmed |
| collection | DOAJ |
| description | The paper discusses a technique for detecting electrical fires in residential buildings using the Gradient Boosting Machine (GBM) algorithm. The features of the algorithm process data related to current, voltage, and total harmonic distortion (THD) from electrical systems, considering resistive loads, such as heating appliances, and inductive loads, like refrigerators and washing machines. The technique underscores the relationship between electrical characteristics and fire risks, demonstrating that the gradient boosting machine can accurately predict fire hazards under various fault conditions, including arc faults, overvoltage, and contact opening. Results from MATLAB simulations confirm the algorithm'sefficacy and high accuracy rates for heating systems and induction motors across different fault types that could lead to electrical fires in buildings. These results highlight the significance of effective feature selection in enhancing the algorithm's performance while addressing some imprecision, particularly regarding the two different load types. Ultimately, the Gradient Boosting Machine represents a promising approach to improving the safety of electrical systems and supporting fire detection strategies. |
| format | Article |
| id | doaj-art-cdfdf38ac5f441fca957c672e8a07405 |
| institution | Kabale University |
| issn | 2707-7799 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Erbil Polytechnic University |
| record_format | Article |
| series | Polytechnic Journal |
| spelling | doaj-art-cdfdf38ac5f441fca957c672e8a074052025-08-20T03:47:16ZengErbil Polytechnic UniversityPolytechnic Journal2707-77992025-06-011517585https://doi.org/10.59341/2707-7799.1860Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine AlgorithmSharaban Taha Ahmed0Fadhil Aula1Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, IraqDepartment of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, IraqThe paper discusses a technique for detecting electrical fires in residential buildings using the Gradient Boosting Machine (GBM) algorithm. The features of the algorithm process data related to current, voltage, and total harmonic distortion (THD) from electrical systems, considering resistive loads, such as heating appliances, and inductive loads, like refrigerators and washing machines. The technique underscores the relationship between electrical characteristics and fire risks, demonstrating that the gradient boosting machine can accurately predict fire hazards under various fault conditions, including arc faults, overvoltage, and contact opening. Results from MATLAB simulations confirm the algorithm'sefficacy and high accuracy rates for heating systems and induction motors across different fault types that could lead to electrical fires in buildings. These results highlight the significance of effective feature selection in enhancing the algorithm's performance while addressing some imprecision, particularly regarding the two different load types. Ultimately, the Gradient Boosting Machine represents a promising approach to improving the safety of electrical systems and supporting fire detection strategies.https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/7gradient boosting machine (gbm),total harmonic distortion (thd),electrical fires,arc faults,machine learning |
| spellingShingle | Sharaban Taha Ahmed Fadhil Aula Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm Polytechnic Journal gradient boosting machine (gbm), total harmonic distortion (thd), electrical fires, arc faults, machine learning |
| title | Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm |
| title_full | Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm |
| title_fullStr | Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm |
| title_full_unstemmed | Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm |
| title_short | Detection of Electrical Fires in Residential Buildings Using a Gradient Boosting Machine Algorithm |
| title_sort | detection of electrical fires in residential buildings using a gradient boosting machine algorithm |
| topic | gradient boosting machine (gbm), total harmonic distortion (thd), electrical fires, arc faults, machine learning |
| url | https://polytechnic-journal.epu.edu.iq/home/vol15/iss1/7 |
| work_keys_str_mv | AT sharabantahaahmed detectionofelectricalfiresinresidentialbuildingsusingagradientboostingmachinealgorithm AT fadhilaula detectionofelectricalfiresinresidentialbuildingsusingagradientboostingmachinealgorithm |