An accurate analysis of the parameters affecting consumption and price fluctuations of electricity in the Iranian market in summer

Document Type : Article


1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran

2 Department of Mathematics, Science and Research Branch, Islamic Azad university, Tehran, Iran

3 Department of Industrial Engineering, Mazandaran University of Science and Technology Branch, Babol, Iran


In this paper, a novel method is proposed to predict the cost of short-term hourly electrical energy based on combined neural networks. In this method, the influential parameters that play a key role in the accuracy of these systems are identified and the most prominent ones are selected. Due to the fluctuations of electricity prices during various seasons and days, these parameters do not adhere to the same pattern. In the proposed method, initially, using the SOM network, similar days are placed in close clusters. In the next stage, the temperature parameter and prices pertaining to similar days are trained separately in two MLP neural networks because of their differences concerning the range of changes and their nature. Finally, the two networks are merged with another MLP network. In the proposed hybrid method, an evolutionary search method is used to provide an appropriate initial weight for neural network training. Given the price data changes, the price amidst the previous hour has a significant effect on the prediction of the current state. In this vein, in the proposed method, the predicted data in the previous hour is considered as one of the inputs of the next stage.


1. Lin, L., Zhou, C., Saritporn, V., Shen, X., and Dong, M. "Opportunities and challenges for biodiesel fuel", Appl Energy, 88(4), pp. 1020-31 (2011).
2. Dhinesh, B., Raj, Y., Ambrose, M., Kalaiselvan, C., and KrishnaMoorthy, R. "A numerical and experimental assessment of a coated diesel engine powered by high-performance nano biofuel", Energy Convers Manage, 171, pp. 815-24 (2018).
3. Vigneswaran, R., Annamalai, K., Dhinesh, B., and Krishnamoorthy, R. "Experimental investigation of unmodified diesel engine performance, combustion and emission with multipurpose additive along with waterin diesel emulsion fuel", Energy Convers Manage X, 172, pp. 370-380 (2018).
4. Dhinesh, B. and Annamalai, M. "A study on performance, combustion and emission behaviour of diesel engine powered by novel nano nerium oleander biofuel", Journal of Cleaner Production, 196, pp. 74-83 2018.
5. Noboru, N., Atsushi, I., Yutaka, T., Makoto, A. "Lifecycle emission of oxidic gases from power-generation systems", Appl Energy, 68(2), pp. 215-227 (2001).
6. Sanchez de la Nieta, A.A., Gonzalez, V., and Contreras, J. "Portfolio decision of short-term electricity forecasted prices through stochastic programming",Energies, 9(12), p. 1069 (2016).
7. Najafi, A., Falaghi, H., Contreras, J., and Ramezani, M. "Medium-term energy hub management subject to electricity price and wind uncertainty", Appl Energy, 168, pp. 418-33 (2016).
8. Yang, P., Tang, G., and Nehorai, A. "A game-theoretic approach for optimal time-of-use electricity pricing", IEEE Trans Power Syst, 28(2), pp. 884-92 (2013).
9. Wang, D., Luo, H., Grunder, O., Lin, Y., and Guo, H. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and bp neural network optimized by fire y algorithm", Appl Energy, 190, pp. 390-407 (2017).
10. Yang, Z., Ce, L., and Lian, L. "Electricity price forecasting by a hybrid model, combining wavelet transform, arma and kernel-based extreme learning machine methods", Appl Energy, 190, pp. 291-305 (2017).
11. Abedinia, O., Amjady, N., Shafie-khah, M., and Catalao, J.P.S. "Electricity price forecast using combinatorial neural network trained by a new stochastic search method", Energy Convers Manage, 105, pp. 642-654 (2015).
12. Lago, J., Ridder, F.D., Vrancx, P., and Schutter, B.D. "Forecasting day-ahead electricity prices in Europe: the importance of considering market integration", Appl Energy, 211, pp. 890-903 (2018).
13. Weron, R. "Electricity price forecasting: a review of the state-of-the-art with a look into the future", Int J Forecast, 30(4), pp. 1030-81 (2014).
14. Lago, J., Ridder, F.D., and Schutter, B.D. "Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms", Appl Energy, 221, pp. 386-405 (2018).
15. Wang, J.Z., Liu, F., Song, Y.L., and Zhao, J. "A novel model: dynamic choice artificial neural network (DCANN) for an electricity price forecasting system", Appl Soft Comput, 48, pp. 281-97 (2016).
16. Bento, P.M.R., Pombo, J.A.N., Calado, M.R.A., and Mariano, S.J.P.S. "A bat optimized neural network and wavelet transform approach for short-term price forecasting", Appl Energy, 210, pp. 88-97 (2018).
17. Ghasemi, A., Shayeghi, H., Moradzadeh, M., and Nooshyar, M. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demandside management", Appl Energy, 177, pp. 40-59 (2016).
18. Mirakyan, A., Meyer-Renschhausen, M., and Koch, A."Composite forecasting approach, application for nextday electricity price forecasting", Energy Econ, 66, pp. 228-237 (2017).
19. Sanjeev Kumar Aggarwal, Lalit Mohan Saini, Ashwani Kumar, "Electricity price forecasting in deregulated markets: A review and evaluation", International Journal of Electrical Power & Energy Systems, 31, pp. 13-22 (2009).
20. Panapakidis, I.P. and Dagoumas, A.S. "Day-ahead electricity price forecasting via the application of artificial neural network based models", Appl Energy, 172, pp. 132-51 (2016).
21. Sandhu, H.S., Fang, L.P., and Guan, L. "Forecasting day-ahead price spikes for the Ontario electricity market", Electr Pow Syst Res, 141, pp. 450-459 (2016).
22. Ortiz, M., Ukar, O., Azevedo, F., and Mugica, A. "Price forecasting and validation in the Spanish electricity market using forecasts as input data", Electr Pow Energy Syst, 77, pp. 123-127 (2016).
23. Keles, D., Scelle, J., Paraschiv, F., and Fichtner, W. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks", Appl Energy, 162, pp. 218-230 (2016).
24. Singh, N., Mohanty, S.R., and Shukla, R.D. "Short term electricity price forecast based on environmentally adapted generalized neuron", Energy, 125, pp. 127-139 (2017).
25. Itaba, S. and Mori, H. "A fuzzy-preconditioned GRBFN model for electricity price forecasting", Proc Comput Sci, 114, pp. 441-448 (2017).
26. Wang, D.Y., Luo, H.Y., Grunder, O., et al. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by fire y algorithm", Appl Energy, 190, pp. 390-407 (2017).
27. Tang, L., Yu, L., and He, K. "A novel datacharacteristic- driven modeling methodology for nuclear energy consumption forecasting", Appl Energy, 128, pp. 1-14 (2014).
28. Chai, J., Zhang, Z.Y., Wang, S.Y., Lai, K.K., and Liu, J. "Aviation fuel demand develop in China", Energy Econ, 46, pp. 224-35 (2014).
29. Diongue, A.K., Guegan, D., and Vignal, B. "Forecasting electricity spot market prices with a k-factor GIGARCH process", Appl Energy, 86, pp. 505-510 (2009).
30. Girish, G.P. "Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models", Energy Strateg Rev, 11, pp. 52-57 (2016).
31. Zhang, J.L. and Tan, Z.F. "Day-ahead electricity price forecasting using WT, CLSSVM and EGARCH model", Electr Pow Energy Syst, 45, pp. 362-368 (2013).
32. Zhang, Y., Li, C., and Li, L. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods", Appl Energy, 190, pp. 291-305 (2017).
33. Yan, X. and Chowdhury, N.A. "Mid-term electricity market clearing price forecasting: a hybrid LSSVM and ARMAX approach", Electr Pow Energy Syst, 53, pp. 20-6 (2013).
34. Zhu, B.Z. and Wei, Y.M. "Carbon price prediction with a hybrid ARIMA and least squares support vector machines methodology", Omega, 41, pp. 517-24 (2013).
35. He, K.J., Yu, L., and Tang, L. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology", Energy, 91, pp. 601-609 (2015).
36. Qiu, X.H., Suganthan, P.N., and Amaratunga, G.A.J. "Short-term electricity price forecasting with empirical mode decomposition based ensemble kernel machines", Proc Comput Sci, 108, pp. 1308-1317 (2018).