Impact of fuel prices on electricity price using the predictive power of ANN-GA, LRM: Evidence from Iran

Document Type : Research Paper


1 MS.c of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Industrial engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

3 Business School, Victoria University, Australia

4 MS.c of Artificial intelligence Engineering, Amirkabir University of Technology, Tehran, Iran


In Iran, the energy price is very much influenced by the dollar price. However, this price is highly fluctuated due to various reasons. The emergence of the pandemic, the covid-19, from one part and the financial sanctions on the economy from another, cause the high volatility on this foreign currency. First, in this study, we converted the IRR (Iranian currency) into the same dollar rate of the year, contributing to the impact of exchange rate volatility in the model. Then, we forecast the price of all three principal fuels that affect the cost of electricity production, and then we forecast the electricity prices using ANN_GA and the historical data. This study also examines the fundamental and exacerbating causes in recent years, especially in 2018 when we faced an unprecedented increase in dollar prices in the Iranian market when the U.S. withdrew from the joint comprehensive plan of action (JCPA), and its effects are still visible. We intend to investigate the impact of these fluctuations on the future electricity market. In the end, we examine that which variables (fuel prices) would affect electricity prices the most using a linear regression model.


Main Subjects

Aggarwal, S. K., Saini, L. M., & Kumar, A. (2009). Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems, 31(1), 13-22.
Alagidede, P., & Ibrahim, M. (2017). On the causes and effects of exchange rate volatility on economic growth: Evidence from Ghana. Journal of African Business, 18(2), 169-193.
Anand, A., & Suganthi, L. (2020). Forecasting of electricity demand by hybrid ANN-PSO models. In Deep learning and neural networks: Concepts, methodologies, tools, and applications (pp. 865-882). IGI Global.
Bin Khamis, A., & Yee, P. H. (2018). A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price. European Journal of Engineering and Technology Research, 3(6), 10-14.
Chinmoy, L., Iniyan, S., & Goic, R. (2019). Modeling wind power investments, policies and social benefits for deregulated electricity market–A review. Applied energy, 242, 364-377.
Dbouk, W., & Jamali, I. (2018). Predicting daily oil prices: Linear and non-linear models. Research in International Business and Finance, 46, 149-165.
Fan, L., Pan, S., Li, Z., & Li, H. (2016). An ICA-based support vector regression scheme for forecasting crude oil prices. Technological Forecasting and Social Change, 112, 245-253.
Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC.
Fitzmaurice, G. M. (2016). Regression. Diagnostic Histopathology, 22(7), 271-278.
Harrell, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis (Vol. 3). New York: springer.
Mahdiani, M. R., & Khamehchi, E. (2016). A modified neural network model for predicting the crude oil price. Intellectual Economics, 10(2), 71-77.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
McDowell, J. J., Calvin, O. L., & Klapes, B. (2016). A survey of residual analysis and a new test of residual trend. Journal of the Experimental Analysis of Behavior, 105(3), 445-458.
Panda, S. K., Ray, P., & Mishra, D. P. (2021). Short term load forecasting using metaheuristic techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 1033, No. 1, p. 012016). IOP Publishing.
Pilleboue, A., Singh, G., Coeurjolly, D., Kazhdan, M., & Ostromoukhov, V. (2015). Variance analysis for Monte Carlo integration. ACM Transactions on Graphics (TOG), 34(4), 1-14.
Rokhsari, A., Esfahanipour, A., & Ardehali, M. M. (2020). Computing optimal subsidies for Iranian renewable energy investments using real options. Journal of Industrial and Systems Engineering, 13(Special issue: 16th International Industrial Engineering Conference), 16-29.
Román-Portabales, A., López-Nores, M., & Pazos-Arias, J. J. (2021). Systematic review of electricity demand forecast using ANN-based machine learning algorithms. Sensors, 21(13), 4544.
Sharma, D. K., Hota, H. S., Brown, K., & Handa, R. (2021). Integration of genetic algorithm with artificial neural network for stock market forecasting. International Journal of System Assurance Engineering and Management, 1-14.
Singh, A., Singh, N. K., & Singh, P. (2020, February). Daily Electric Forecast for Various Indian Regions Using ANN. In 2020 International Conference on Electrical and Electronics Engineering (ICE3) (pp. 95-100). IEEE.
Singhal, D., & Swarup, K. S. (2011). Electricity price forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 550-555.
Sompui, M., & Wongsinlatam, W. (2014). Prediction Model for Crude Oil Price Using Artificial Neural Networks. Applied Mathematical Sciences, 8(80), 3953-3965.
Tondolo de Miranda, S., Abaide, A., Sperandio, M., Santos, M. M., & Zanghi, E. (2018). Application of artificial neural networks and fuzzy logic to longā€term load forecast considering the price elasticity of electricity demand. International Transactions on Electrical Energy Systems, 28(10), e2606.
Viegas, J. L., Vieira, S. M., Melício, R., Mendes, V. M., & Sousa, J. (2016, April). GA-ANN short-term electricity load forecasting. In Doctoral Conference on Computing, Electrical and Industrial Systems (pp. 485-493). Springer, Cham.
Villar, J. A., & Joutz, F. L. (2006). The relationship between crude oil and natural gas prices. Energy Information Administration, Office of Oil and Gas, 1, 1-43.
Zhang, B., & Ma, J. (2011). Coal price index forecast by a new partial least-squares regression. Procedia Engineering, 15, 5025-5029.