Forecast for Market Clearing Price with Artificial Neural Networks in Day Ahead Market

In this study, the Market Clearing Price (MCP) is forecasted with Artificial Neural Networks and the modeling success is examined for different preprocessing strategies. The purpose of the study is to obtain the optimum model with a significant estimation success and to provide the best price prediction. The hour-based electricity generation data of diverse production items are assigned as inputs and the resulting MCP is modeled. The raw data are first cleaned from outliers, then subjected to different normalization processes and 70 different ANNs are trained. Additionally, networks are trained with data classified in seasons and the effect of seasonal patterns on model success is observed. Finally, networks showing the optimum performance are selected. It is noted that the type of the normalization strategy and the hidden layer size are the key factors to make a decent estimation. Then, in order to test the networks with extreme cases, data for the special days (official holidays) are applied to these networks as input. The success of the networks is evaluated by comparing the MCP predictions with the actual values. It is revealed to make a prediction for official holidays, a model which is special to this period of year is required.

PDF

___

[1] Y. Biçen, “Türkiye elektrik enerjisi piyasası gelişim süreci: Gün öncesi ve dengeleme güç piyasası özellikleri.” Karaelmas Sci. Eng. J., vol. 6. 2, 2016, pp. 432–438.
[2] Electricity market Law No: 28603, Official Gazette of the Republic of Turkey, 6446, 5 (53). March 2013.
[3] 2017 EPİAŞ Activity report of the board. 2 March 2018; Available from: https://www.epias.com.tr/en/corporate/annual-reports/.
[4] P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method.” IEEE Trans. Power Syst., vol. 22. 4, 2007, pp. 2058–2065.
[5] I. Y. Zolotova and V. V Dvorkin, “Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks.” Stud. Russ. Econ. Dev., vol. 28. 6, 2017, pp. 608–615.
[6] K. K. Nargale and S. B. Patil, “Day ahead price forecasting in deregulated electricity market using Artificial Neural Network.” in 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), India, 2016.
[7] F. Saâdaoui, “A seasonal feedforward neural network to forecast electricity prices.” Neural Comput. Appl., vol. 28. 4, 2017, pp. 835–847.
[8] D. Keles, J. Scelle, F. Paraschiv, and W. Fichtner, “Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks.” Appl. Energy, vol. 162, 2016, pp. 218–230.
[9] F. Şenocak and H. Kahveci, “Periodic price avarages forecasting of MCP in day-ahead market.” in National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Turkey, 2016.
[10] A. Dalgın, “Yapay Sinir Ağları Kullanılarak Türkiye Gün Öncesi Piyasası Elektrik Fiyat Tahmini.” ITU Energy Institute, Turkey, 2017.
[11] F. Gao, X. Guan, X. Cao and A. Papalexopoulos, "Forecasting power market clearing price and quantity using a neural network method." in 2000 Power Engineering Society Summer Meeting, WA, USA, 2000.
[12] P.S. Georgilakis, "Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks." in 4 th Hellenic Conference on Artificial Intelligence, Greece, 2006.
[13] L. Zhang and P. B. Luh, "Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method." IEEE Trans. on Power Systems, vol. 20, no. 1, 2005, pp. 59-66.
[14] N. M. Pindoriya, S. N. Singh and S. K. Singh, "An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets." IEEE Trans. on Power Systems, vol. 23, no. 3, 2008, pp. 1423-1432.
[15] D. Singhal, K.S. Swarup, "Electricity price forecasting using artificial neural networks." International Journal of Electrical Power & Energy Systems, vol. 33, Issue 3, 2011, pp. 550-555.
[16] I. N. Da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, “Artificial neural network architectures and training processes,” in Artificial neural networks, Springer, 2017, pp. 21–28.
[17] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey.” Heliyon, vol. 4. 11, 2018, pp. e00938.
[18] R. Hecht-Nielsen, “Theory of the backpropagation neural network,” in Neural networks for perception, Elsevier, 1992, pp. 65–93.