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Comparison of machine learning based methods for residential load forecasting

author
Shabbir, Noman
Ahmadiahangar, Roya
Kütt, Lauri
Rosin, Argo
statement of authorship
Noman Shabbir, Roya Ahmadiahangar, Lauri Kütt, Argo Rosin
source
2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM), Kärdla, Estonia, June 12-15, 2019 : proceedings
location of publication
[S.l.]
publisher
IEEE
year of publication
2019
pages
4 p. : ill
conference name, date
2019 Electric Power Quality and Supply Reliability Conference together with 2019 Symposium on Electrical Engineering and Mechatronics, June 12-15, 2019
conference location
Kärdla, Estonia
url
https://doi.org/10.1109/PQ.2019.8818267
subject term
energiasüsteemid
elektrivõrgud
tehisõpe
talupidamine
subject of form
konverentsikogumikud
keyword
load forecasting
machine learning
household
regression
SVM
notes
Bibliogr.: 19 ref
TalTech department
elektroenergeetika ja mehhatroonika instituut
language
inglise
Reserch Group
Fundamentals of electrical engineering
Microgrids and metrology
Smart city research group
Shabbir, N., Ahmadiahangar, R., Kütt, L., Rosin, A. Comparison of machine learning based methods for residential load forecasting // 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM), Kärdla, Estonia, June 12-15, 2019 : proceedings. [S.l.] : IEEE, 2019. 4 p. : ill. https://doi.org/10.1109/PQ.2019.8818267