Hybrid Attention-Based LSTM and XGBoost Model for Short-Term Residential Load Forecasting
author
Shabbir, Noman
Shahid, Arqum
Daniel, Kamran
Jawad, M.
Rosin, Argo
Martins, Joao
statement of authorship
Noman Shabbir, Arqum Shahid, Kamran Daniel, Muhammad Jawad, Argo Rosin, Joao Martins
source
2025 IEEE the 13th International Conference on Smart Energy Grid Engineering (SEGE 2025)
location of publication
Piscataway
publisher
IEEE
year of publication
2025
pages
p. 94-99
conference name, date
IEEE the 13th International Conference on Smart Energy Grid Engineering (SEGE 2025) 18-20 August
conference location
Oshawa, Canada
url
https://doi.org/10.1109/SEGE65970.2025.11203473
subject term
tehisõpe
tehisnärvivõrgud
tarkvara
Scopus
https://www.scopus.com/pages/publications/105021828371?origin=resultslist
keyword
deep learning
LSTM
machine learning
residential load forecasting
ISBN
979-833158592-1
notes
Bibliogr.: 13 ref
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