A LONG SHORT TERM MEMORY APPLICATION ON THE TURKISH INTRADAY ELECTRICITY PRICE FORECASTING

Authors : Hakan YORULMUS, Umut UGURLU, Oktay TAS

Pages : 126-130

Doi:10.17261/Pressacademia.2018.867

View : 13 | Download : 8

Publication Date : 2018-09-01

Article Type : Research Paper

Abstract :Purpose- This paper aims to forecast the Turkish intraday electricity prices accurately. It will be the first intraday electricity price forecasting work, which uses Long-Short Term Memory insert ignore into journalissuearticles values(LSTM); application. Methodology- LSTM method is based on a special kind of neural network, which is capable of learning long-term dependencies. This paper aims to achieve the best forecasts, in terms of Mean Absolute Error insert ignore into journalissuearticles values(MAE); and Root Mean Square Error insert ignore into journalissuearticles values(RMSE);, by applying the LSTM model with multistep-ahead prediction approach. Findings- LSTM model created in this study performed better with lagged values, electricity consumption and electricity production values. Especially using the lagged values of the prices and the reserve margin gave successful results. Conclusion- The proposed method has improvement in the accuracy of forecasting. Turkish Intraday Electricity Market needs further research with time series methods as well as other neural network models
Keywords : Electricity price forecasting, Turkish intraday electricity market, LSTM, multistep ahead prediction, deep learning, time series