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

Translated title of the contribution: A RNN based time series approach for forecasting turkish electricity load

Alper Tokgoz, Gozde Unal

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

109 Citations (Scopus)

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.

Translated title of the contribution	A RNN based time series approach for forecasting turkish electricity load
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404313
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

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

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404313

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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

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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",

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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, Turkey, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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