A unified framework for siting and sizing of distributed energy resources in power distribution grids using data-driven machine learning optimization

•Machine learning applied for optimal siting and sizing of DG and ESS in power grids.•Decision tree and random forest algorithms improve planning accuracy.•Voltage profile improved by 11.92 %, power losses reduced by 36.88 %.•Unserved energy reduced by 59.13 %, recovery time shortened by 30.48 %.•Va...

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Veröffentlicht in:	International journal of electrical power & energy systems Jg. 172; S. 111214
1. Verfasser:	Ghanbari, Sanaz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2025 Elsevier
Schlagworte:	Data-driven optimization Decision tree algorithm Machine learning Optimal siting and sizing Random forest algorithm Supervised learning Decision tree algorithm Random forest algorithm Supervised learning Optimal siting and sizing Machine learning Data-driven optimization
ISSN:	0142-0615
Online-Zugang:	Volltext
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