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, Türkiye, 2 - 05 Mayıs 2018 identifier identifier


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.