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

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 (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 (MAE) and Root Mean Square Error (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

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