Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi

Alper Tokgoz; Gozde Unal

doi:10.1109/SIU.2018.8404313

Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi

Alper Tokgoz
, Gozde Unal

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

117 Atıf (Scopus)

Özet

RNN, LSTM and GRU variations have been increasing its popularity on time-series applications. Liberalization of Turkish Electricity Market empowers the necessity of better electricity consumption prediction systems. This paper presents a Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) based time series forecasting experiments on Turkish electricity load prediction. Resulting %0.71 MAPE success of our experiments yields better results than existing researches based on ARIMA and artificial neural networks on Turkish electricity load forecasting which have %2.6 and %1.8 success rate respectively.

Tercüme edilen katkı başlığı	A RNN based time series approach for forecasting turkish electricity load
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404313
Yayın durumu	Yayınlandı - 5 Tem 2018
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Türkiye Süre: 2 May 2018 → 5 May 2018

Yayın serisi

Adı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Türkiye
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

Electric load forecasting
Gru
Lstm
Rnn
Time series prediction
Turkisk electricity market

Belgeye Erişim

10.1109/SIU.2018.8404313

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Tokgoz, A., & Unal, G. (2018). Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404313

@inproceedings{f24f8943d3814d3a93e37f6a36076925,

title = "T{\"u}rkiye Elektrik T{\"u}ketimi Tahmini I{\c c}in RNN Tabanli Zaman Serisi Yakl{\c a}simi",

abstract = "RNN, LSTM and GRU variations have been increasing its popularity on time-series applications. Liberalization of Turkish Electricity Market empowers the necessity of better electricity consumption prediction systems. This paper presents a Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) based time series forecasting experiments on Turkish electricity load prediction. Resulting \%0.71 MAPE success of our experiments yields better results than existing researches based on ARIMA and artificial neural networks on Turkish electricity load forecasting which have \%2.6 and \%1.8 success rate respectively.",

keywords = "Electric load forecasting, Gru, Lstm, Rnn, Time series prediction, Turkisk electricity market",

author = "Alper Tokgoz and Gozde Unal",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

month = jul,

day = "5",

doi = "10.1109/SIU.2018.8404313",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

booktitle = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

address = "United States",

}

Tokgoz, A & Unal, G 2018, Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Türkiye, 2/05/18. https://doi.org/10.1109/SIU.2018.8404313

Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi. / Tokgoz, Alper; Unal, Gozde.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi

AU - Tokgoz, Alper

AU - Unal, Gozde

PY - 2018/7/5

Y1 - 2018/7/5

N2 - RNN, LSTM and GRU variations have been increasing its popularity on time-series applications. Liberalization of Turkish Electricity Market empowers the necessity of better electricity consumption prediction systems. This paper presents a Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) based time series forecasting experiments on Turkish electricity load prediction. Resulting %0.71 MAPE success of our experiments yields better results than existing researches based on ARIMA and artificial neural networks on Turkish electricity load forecasting which have %2.6 and %1.8 success rate respectively.

AB - RNN, LSTM and GRU variations have been increasing its popularity on time-series applications. Liberalization of Turkish Electricity Market empowers the necessity of better electricity consumption prediction systems. This paper presents a Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) based time series forecasting experiments on Turkish electricity load prediction. Resulting %0.71 MAPE success of our experiments yields better results than existing researches based on ARIMA and artificial neural networks on Turkish electricity load forecasting which have %2.6 and %1.8 success rate respectively.

KW - Electric load forecasting

KW - Gru

KW - Lstm

KW - Rnn

KW - Time series prediction

KW - Turkisk electricity market

UR - https://www.scopus.com/pages/publications/85050829081

U2 - 10.1109/SIU.2018.8404313

DO - 10.1109/SIU.2018.8404313

M3 - Konferans katkısı

AN - SCOPUS:85050829081

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

Türkiye Elektrik Tüketimi Tahmini Için RNN Tabanli Zaman Serisi Yakląsimi

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap