Machine Learning-Based Forecasting Active Power Loss in Distribution Systems
This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus volt...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/26/e3sconf_eier2025_04003.pdf |
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| author | Haider Waseem Batool Seema Milazzo Federica Ha Quang P. |
| author_facet | Haider Waseem Batool Seema Milazzo Federica Ha Quang P. |
| author_sort | Haider Waseem |
| collection | DOAJ |
| description | This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss. |
| format | Article |
| id | doaj-art-892e9924f20b4d1c8ec303912e12be45 |
| institution | OA Journals |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-892e9924f20b4d1c8ec303912e12be452025-08-20T01:53:30ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016260400310.1051/e3sconf/202562604003e3sconf_eier2025_04003Machine Learning-Based Forecasting Active Power Loss in Distribution SystemsHaider Waseem0Batool Seema1Milazzo Federica2Ha Quang P.3Faculty of Engineering and IT, University of Technology SydneyDepartment of Development and Environmental Studies, Paris-Saclay UniversityFaculty of Engineering and IT, University of Technology SydneyFaculty of Engineering and IT, University of Technology SydneyThis paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/26/e3sconf_eier2025_04003.pdfdistributed generationactive power lossforecastinggradient boosting machines regression |
| spellingShingle | Haider Waseem Batool Seema Milazzo Federica Ha Quang P. Machine Learning-Based Forecasting Active Power Loss in Distribution Systems E3S Web of Conferences distributed generation active power loss forecasting gradient boosting machines regression |
| title | Machine Learning-Based Forecasting Active Power Loss in Distribution Systems |
| title_full | Machine Learning-Based Forecasting Active Power Loss in Distribution Systems |
| title_fullStr | Machine Learning-Based Forecasting Active Power Loss in Distribution Systems |
| title_full_unstemmed | Machine Learning-Based Forecasting Active Power Loss in Distribution Systems |
| title_short | Machine Learning-Based Forecasting Active Power Loss in Distribution Systems |
| title_sort | machine learning based forecasting active power loss in distribution systems |
| topic | distributed generation active power loss forecasting gradient boosting machines regression |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/26/e3sconf_eier2025_04003.pdf |
| work_keys_str_mv | AT haiderwaseem machinelearningbasedforecastingactivepowerlossindistributionsystems AT batoolseema machinelearningbasedforecastingactivepowerlossindistributionsystems AT milazzofederica machinelearningbasedforecastingactivepowerlossindistributionsystems AT haquangp machinelearningbasedforecastingactivepowerlossindistributionsystems |