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

author
Shabbir, Noman
Husev, Oleksandr
Daniel, Kamran
Jawad, Muhammad
Rosin, Argo
Martins, Joao
statement of authorship
Noman Shabbir, Oleksandr Husev, Kamran Daniel, Muhammad Jawad, Argo Rosin, Joao Martins
source
2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
publisher
IEEE
year of publication
2024
pages
6 p
conference name, date
18th International Conference on Compatibility, Power Electronics and Power Engineering, June 24 - 26, 2024
conference location
Gdynia, Poland
url
https://doi.org/10.1109/CPE-POWERENG60842.2024.10604392
subject term
elektritarbimine
algoritmid
tehisõpe
alalisvool
elektritarbimine
Scopus
scopus
keyword
residential load
load forecasting
DC loads
machine learning (ML)
XGBoost
ISBN
979-8-3503-1826-5
notes
Bibliogr.: 27 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
Targa linna tippkeskus
elektroenergeetika ja mehhatroonika instituut
language
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