XGBoost based day ahead solar energy generation forecasting using trends and periodicity features in historical and weather data
author
Shabbir, Noman
Daniel, Kamran
Rosin, Argo
Raja, Hadi Ashraf
Jawad, Muhammad
Martins, Joao
statement of authorship
Noman Shabbir, Kamran Daniel, Argo Rosin, Hadi Ashraf Raja, Muhammad Jawad, Joao Martins
source
2025 IEEE XXXII International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
publisher
IEEE
year of publication
2025
pages
6 p
conference name, date
XXXII International Conference on Electronics, Electrical Engineering and Computing (INTERCON 2025), August 20-22, 2025
conference location
Arequipa, Peru
url
https://doi.org/10.1109/INTERCON67304.2025.11244700
subject term
päikeseenergia
prognoosimine (majandus)
tehisõpe
algoritmid
keyword
solar energy
energy forecasting
machine learning (ML)
XGBoost
ISBN
979-8-3315-9993-5
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
FinEst Centre for Smart Cities
Department of Electrical Power Engineering and Mechatronics
FinEst Targa linna tippkeskus
elektroenergeetika ja mehhatroonika instituut
language
English
Inglise