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Generative adversarial network and CNN-LSTM based short-term power load forecasting

author
Liu, Y.
Liang, Z.
Li, X.
Bakeer, Abualkasim Ahmed Ali
statement of authorship
Yushan Liu, Zhouchi Liang, Xiao Li, Abualkasim Bakeer
source
2023 IEEE 17th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
publisher
IEEE
year of publication
2023
conference name, date
2023 IEEE 17th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), 14-16 June 2023
conference location
Tallinn, Estonia
url
https://doi.org/10.1109/CPE-POWERENG58103.2023.10227473
subject term
prognoosmudelid
tehisnärvivõrgud
lühiajaline mälu
prognostika
aegridade analüüs
keyword
Generative Adversarial Network (GaN)
convolutional neural networks (CNNa)
long short-term memory (LSTM)
load forecasting
time series
ISSN
2166-9546
ISBN
979-8-3503-0004-8
notes
Bibliogr.: 11 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
elektroenergeetika ja mehhatroonika instituut
language
inglise
Uurimisrühm
Power electronics group
Liu, Y., Liang, Z., Li, X., Bakeer, A. Generative adversarial network and CNN-LSTM based short-term power load forecasting // 2023 IEEE 17th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). : IEEE, 2023. https://doi.org/10.1109/CPE-POWERENG58103.2023.10227473