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Forecasting household demand-side flexibility using XGBoost for smart energy management

author
Qadar, Rana Muhammad Arslan
Shabbir, Noman
Kütt, Lauri
Daniel, Kamran
Peterson, Kristjan
Rosin, Argo
statement of authorship
Rana Muhammad Arslan Qadar, Noman Shabbir, Lauri Kutt, Kamran Daniel, Kristjan Peterson, Argo Rosin
source
2025 IEEE 66th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)
publisher
IEEE
year of publication
2025
pages
7 p
conference name, date
66th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), October 23-25, 2025
conference location
Riga, Latvia
url
https://doi.org/10.1109/RTUCON67996.2025.11415089
subject term
elektrienergia
paindlikkus
prognostika
regressioonanalüüs
tehisõpe
Scopus
Article at Scopus
keyword
demand-side flexibility
forecasting
machine learning (ML)
regression techniques
XGBoost
ISSN
2768-3338
2996-1033
ISBN
979-8-3315-7772-8
979-8-3315-7773-5
scientific publication
teaduspublikatsioon
classifier
3.1
TalTech department
Department of Electrical Power Engineering and Mechatronics
FinEst Centre for Smart Cities
language
English
Qadar, R. M. A., Shabbir, N., Kütt, L., Daniel, K., Peterson, K., Rosin, A. Forecasting household demand-side flexibility using XGBoost for smart energy management // 2025 IEEE 66th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). : IEEE, 2025. 7 p. https://doi.org/10.1109/RTUCON67996.2025.11415089