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

Document Type : Article

Authors

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

Abstract

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.

Keywords


References
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
unmodi ed 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. Naja , 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  re
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., Sha e-khah, M., and
Catal~ao, 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 arti cial 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 arti -
cial neural network based models", Appl Energy, 172,
pp. 132{51 (2016).
S.M. Kavoosi Davoodi et al./Scientia Iranica, Transactions E: Industrial Engineering 28 (2021) 3538{3550 3549
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 arti cial 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  re
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).