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

Estimation of the Electricity Supply and Demand of Türkiye Employing Deep Learning Networks

“…Similarly, the lagged electricity price, temperature and the economic factors are used to model the hourly electricity demand of Türkiye in another study for the 2013-2015 period using multilayer perceptron, long short-term memory and the gated recurrent unit based models and it is shown that the gated recurrent unit based model gives accurate results (Ugurlu et al, 2018a). Long short-term memory is also used in another work for the estimation of the hourly price of the electricity demand of Türkiye for the period of 2017-2018 and the mean absolute percentage error of 0.24 is achieved (Ugurlu et al, 2018b). Similarly, modelling the hourly electricity demand of Türkiye for the 2012-2014 period is investigated in another study using multilayer perceptron, gradient-descent momentum, Levenberg-Marquardt algorithm and Broyden-Fletcher-Goldfarb-Shanno algorithm and it is concluded that the Broyden-Fletcher-Goldfarb-Shanno algorithm provides accurate results (Gokgoz and Filiz, 2020).…”

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

“…The result of the variable relative importance analysis is presented in descending order in Figure 4. According to the literature, since the impacts of the different lag-orders of the electricity price have a significant impact on the electricity prices, the 1-day (EP_1), 1-week (EP_7), and 2-week (EP_14) lags of the electricity prices were included in the model [50,51]. The most influencing factor affecting electricity prices is renewable electricity in both periods.…”