A RNN Based Time Series Approach for Forecasting Turkish Electricity Load

Tokgoz A., Ünal G.

26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2 - 05 May 2018 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2018.8404313
  • City: İzmir
  • Country: Turkey
  • Istanbul Technical University Affiliated: Yes

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.