prindi

Short-term residental DC load forecasting using extreme gradient boost (XgBoost) algorithm

autor
Shabbir, Noman
Husev, Oleksandr
Daniel, Kamran
Jawad, Muhammad
Rosin, Argo
Martins, Joao
vastutusandmed
Noman Shabbir, Oleksandr Husev, Kamran Daniel, Muhammad Jawad, Argo Rosin, Joao Martins
allikas
2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
kirjastus/väljaandja
IEEE
ilmumisaasta
2024
leheküljed
6 p
konverentsi nimetus, aeg
18th International Conference on Compatibility, Power Electronics and Power Engineering, June 24 - 26, 2024
konverentsi toimumispaik
Gdynia, Poland
leitav
https://doi.org/10.1109/CPE-POWERENG60842.2024.10604392
märksõna
elektritarbimine
algoritmid
tehisõpe
alalisvool
elektritarbimine
Scopus
scopus
võtmesõna
residential load
load forecasting
DC loads
machine learning (ML)
XGBoost
ISBN
979-8-3503-1826-5
märkused
Bibliogr.: 27 ref
teaduspublikatsioon
teaduspublikatsioon
klassifikaator
3.1
TTÜ struktuuriüksus
Targa linna tippkeskus
elektroenergeetika ja mehhatroonika instituut
keel
inglise
Shabbir, N., Husev, O., Daniel, K., Jawad, M., Martins, J. Short-term residental DC load forecasting using extreme gradient boost (XgBoost) algorithm // 2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). : IEEE, 2024. 6 p. https://doi.org/10.1109/CPE-POWERENG60842.2024.10604392