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Forecasting short term wind energy generation using machine learning

author
statement of authorship
Noman Shabbir, Roya AhmadiAhangar, Lauri Kütt, Muhamamd N. Iqbal, Argo Rosin
source
location of publication
Danvers
publisher
year of publication
pages
4 p
conference name, date
2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), October 7-9, 2019
conference location
Riga, Latvia
url
ISBN
978-1-7281-3942-5
notes
Bibliogr.: 19 ref
TTÜ department
language
inglise
subject term
keyword
energy forecast wind energy
machine learning
SVM
Reserch Group
Fundamentals of electrical engineering
Microgrids and metrology
Smart city research group
Shabbir, N., Ahmadiahangar, R., Kütt, L., Iqbal, M. N., Rosin, A. Forecasting short term wind energy generation using machine learning // 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 7-9 October 2019 : conference proceedings. Danvers : IEEE, 2019. 4 p. https://doi.org/10.1109/RTUCON48111.2019.8982365