print

Forecasting short term wind energy generation using machine learning

author
Shabbir, Noman
Ahmadiahangar, Roya
Kütt, Lauri
Iqbal, Muhammad Naveed
Rosin, Argo
statement of authorship
Noman Shabbir, Roya AhmadiAhangar, Lauri Kütt, Muhamamd N. Iqbal, Argo Rosin
source
2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 7-9 October 2019 : conference proceedings
location of publication
Danvers
publisher
IEEE
year of publication
2019
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
https://doi.org/10.1109/RTUCON48111.2019.8982365
subject term
tuuleenergia
prognostika
tehisõpe
keyword
energy forecast wind energy
machine learning
SVM
ISBN
978-1-7281-3942-5
notes
Bibliogr.: 19 ref
TTÜ department
elektroenergeetika ja mehhatroonika instituut
language
inglise
Uurimisrühm
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