Machine Learning-Based Forecasting Active Power Loss in Distribution Systems

Haider, W; Batool, S; Milazzo, F; Ha, QP

Machine Learning-Based Forecasting Active Power Loss in Distribution Systems

Haider, W Batool, S Milazzo, F Ha, QP

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Publisher:: EDP Sciences
Publication Type:: Journal Article
Citation:: E3S Web of Conferences, 2025, 626, pp. 04003
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Haider, W
dc.contributor.author	Batool, S
dc.contributor.author	Milazzo, F
dc.contributor.author	Ha, QP
dc.contributor.editor	Truong, N-V
dc.contributor.editor	Ha, QP
dc.date.accessioned	2025-05-06T10:20:09Z
dc.date.available	2025-05-06T10:20:09Z
dc.date.issued	2025
dc.identifier.citation	E3S Web of Conferences, 2025, 626, pp. 04003
dc.identifier.issn	2555-0403
dc.identifier.issn	2267-1242
dc.identifier.uri	http://hdl.handle.net/10453/187210
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	en
dc.publisher	EDP Sciences
dc.relation.ispartof	E3S Web of Conferences
dc.relation.isbasedon	10.1051/e3sconf/202562604003
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning-Based Forecasting Active Power Loss in Distribution Systems
dc.type	Journal Article
utslib.citation.volume	626
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-05-06T10:20:06Z
pubs.publication-status	Published
pubs.volume	626

Abstract:

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.

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http://hdl.handle.net/10453/187210