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...

Celý popis

Podrobná bibliografia

Vydané v:	International journal of electrical power & energy systems Ročník 172; s. 111214
Hlavný autor:	Ghanbari, Sanaz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.11.2025 Elsevier
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!