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

Year 2018, Volume: 7 Issue: 1, 126 - 130, 01.09.2018

Hakan Yorulmus Umut Ugurlu Oktay Tas

https://doi.org/10.17261/Pressacademia.2018.867

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

References

• Cheng, H., Tan, P. N., Gao, J., Scripps, J. (2006, April). Multistep-ahead time series prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 765-774). Springer, Berlin, Heidelberg.
• Díaz, G., Planas, E. (2016). A note on the normalization of Spanish electricity spot prices. IEEE Transactions on Power Systems, 31(3), 2499-2500.
• EPIAS (2016). Annual Report. 01 January 2016 – 31 December 2016.
• Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., Schmidhuber, J. (2017). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
• Hagemann, S. (2013). Price determinants in the German intraday market for electricity: an empirical analysis.
• Hagemann, S., Weber, C. (2015). Trading volumes in intraday markets: theoretical reference model and empirical observations in selected European markets (No. 03/15). EWL working paper.
• Holm, T. B. (2017). The future importance of short term markets: an analyse of intraday prices in the Nordic intraday market; Elbas (Master's thesis, Norwegian University of Life Sciences, Ås).
• Hryshchuk, A., Lessmann, S. (2018). Deregulated day-ahead electricity markets in Southeast Europe: price forecasting and comparative structural analysis.
• Kiesel, R., Paraschiv, F. (2017). Econometric analysis of 15-minute intraday electricity prices. Energy Economics, 64, 77-90.
• Klæboe, G., Eriksrud, A. L., Fleten, S. E. (2015). Benchmarking time series based forecasting models for electricity balancing market prices. Energy Systems, 6(1), 43-61.
• Kuo, P. H., Huang, C. J. (2018). A high precision artificial neural networks model for short-term energy load forecasting. Energies, 11(1), 213.
• Lago, J., De Ridder, F., De Schutter, B. (2018). Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Applied Energy, 221, 386-405.
• Märkle-Huß, J., Feuerriegel, S., Neumann, D. (2018). Contract durations in the electricity market: causal impact of 15min trading on the EPEX SPOT market. Energy Economics, 69, 367-378.
• Monteiro, C., Ramirez-Rosado, I. J., Fernandez-Jimenez, L. A., Conde, P. (2016). Short-term price forecasting models based on artificial neural networks for intraday sessions in the iberian electricity market. Energies, 9(9), 721.
• Panagiotelis, A., Smith, M. (2008). Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions. International Journal of Forecasting, 24(4), 710-727.
• Pape, C., Hagemann, S., Weber, C. (2016). Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market. Energy Economics, 54, 376-387.
• Scharff, R., Amelin, M. (2016). Trading behaviour on the continuous intraday market Elbas. Energy Policy, 88, 544-557.
• Ugurlu, U., Oksuz, I., Tas, O. (2018). Electricity price forecasting using recurrent neural networks. Energies, 11(5), 1255.
• Uniejewski, B., Nowotarski, J., Weron, R. (2016). Automated variable selection and shrinkage for day-ahead electricity price forecasting. Energies, 9(8), 621.
• Weron, R. (2014). Electricity price forecasting: a review of the state-of-the-art with a look into the future. International journal of forecasting, 30(4), 1030-1081.