Forecasting electricity demand in Tehran province for Consumer Sectors

Document Type : Original Article

Authors

1 Ph.D. Student in Biosystems Mechanical Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Professor, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Assistant Professor, Department of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

To meet the needs of life, various types of energy carriers are needed, of which electricity is the most important. Planning to develop electricity generation capacity, requires foresight and long-term forecasting of electricity demand. The objective of this study is to forecast electricity consumption in Tehran province for the time horizon of 2033. Because the parameters affecting electricity demand in different consumer sectors are different, it is better to separate these sectors from each other; So, Tehran province's electricity consumption data is collected for consumer sectors (residential, commercial, agricultural, industrial and public) for a 20-year statistical period (2004-2023) and ARIMA model is used for their estimation and forecasting. In this study, all time series are stationary in first order of difference and according to the Box-Jenkins method, the best ARIMA model is selected for each of the time series. The results show that electricity consumption in Tehran province will increase from 42.1 TWh in 2023 to 71.2 TWh in 2033 and the share of the residential and commercial sectors is more than 50%. Also, it is predicted that the consumption of the commercial sector will be higher than the consumption of the public sector.

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References

[1] Simaee M & Mirabadi A. Modeling short-term forecast of electricity consumption using artificial intelligence methods in Gilan province. Journal of Sustainable Energy Systems. 2022; 1(3):209-230. (Persian) DOI: https://doi.org/10.22059/ses.2023.350868.1015
[2] Wei N, Li C, Peng X, Zeng F & Lu X. Conventional models and artificial intelligence-based models for energy consumption forecasting: A review. Journal of Petroleum Science and Engineering. 2019; 181:106187. DOI: https://doi.org/10.1016/j.petrol.2019.106187
[3] Zahaki Rahat M & Sadeghi saghdel H. Modeling and short-term prediction of national electricity consumption using recurrent neural network and TPE optimization algorithm. Quarterly Energy Economics Review. 2024; 20(83):159-182. (Persian)
[4] Rostami M, Khademvatani A & Omidali M. Forecasting electricity demand in Iran: The application of a hybrid dynamic partial adjustment and ARIMA model. Quarterly Journal of Applied Economics Studies Iran. 2018; 7(25):177-199. (Persian)
[5] Hammad M A, Jereb B, Rosi B & Dragan D. Methods and models for electric load forecasting: a comprehensive review. Logistics & Sustainable Transport. 2020; 11(1):51-76. DOI: https://doi.org/10.2478/jlst-2020-0004
[6] Mir A A, Alghassab M, Ullah K, Khan Z A, Lu Y & Imran M. (2020). A review of electricity demand forecasting in low and middle income countries: The demand determinants and horizons. Sustainability. 2020; 12(15):5931. DOI: https://doi.org/10.3390/su12155931
[7] Zellner M, Abbas A E, Budescu D V & Galstyan A. A survey of human judgement and quantitative forecasting methods. Royal Society Open Science. 2021; 8(2):201187. DOI: https://doi.org/10.1098/rsos.201187
[8] Abbaspour Ghadim Bonab A. A comparative study of demand forecasting based on machine learning methods with time series approach. Journal of Applied Research on Industrial Engineering. 2022; 9(3):331-353. DOI: https://doi.org/10.22105/jarie.2021.246283.1192
[9] Jamil R. Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030. Renewable Energy. 2020; 154:1-10. DOI: https://doi.org/10.1016/j.renene.2020.02.117
[10] Yavary K & Zolfaghari M. Modeling and forecasting the country's short-term electricity consumption using neural networks and wavelet transform (with emphasis on environmental and climatic effects). Quarterly Energy Economics Review. 2012; 9(33):1-29. (Persian)
[11] Jalaee S A, Jafari S & Ansari Lari S. The Estimation of electricity consumption in the residential sector in Iran: A provinces panel. Journal of Iranian Energy Economics. 2013; 2(8):69-92. (Persian)
[12] Hamidizadeh M, Kargar M & Hamidian M. Presenting the prediction model of Iran’s electricity annual consumption by means of narx neural network and studying effect of targeted subsidies on it. Journal of Economics and Modelling. 2014; 4(16):89-113. (Persian)
[13] Amadeh H, mehregan N, haghani M & haddad M. Estimation of Electricity demand structural model in the agricultural sector using Underlying Trend concept and Kalman filter algorithm. Quarterly Energy Economics Review 2014; 10(42):109-134. (Persian)
[14] Razavi S A & Ahmadi M T. Investigating the factors influencing the demand for electricity in service providing sector using Firefly and Cuckoo algorithms. Journal of Economics and Modelling. 2014; 5(17-18):111-134. (Persian)
[15] Diba S & Gholizadeh H. Residential electricity demand in East Azerbaijan province. 1st International Conference on Economic Planning, Sustainable and Balanced Regional Development; Approaches and Applications. Department of Economics, University of Kurdistan, Sanandaj, Iran. 03-04 May 2017. (Persian)
[16] Qharabaghi S & Emami Meibod A. Estimation and study of Iran's electricity demand function in the three sectors of industry, household and agriculture. Economic Journal. 2013; 17(7):23-39. (Persian)
[17] Salmani A & Mojarrad F. Analysis of relationship between climatic variables and electricity consumption and estimated demand by general circulation models in western Iran. Journal of Physical Geography Research. 2019; 51(2):301-315. (Persian) DOI: 10.22059/jphgr.2019.271997.1007320
[18] Kazemi A, Bashirzadeh R & Aryaee S. Iran's Electrical Energy Demand Forecasting Using Meta-Heuristic Algorithms. Iranian Journal of Energy. 2020; 22(4):27-44. (Persian)
[19] Fattah J, Ezzine L, Aman Z, Moussami, H E & Lachhab A. Forecasting of demand using ARIMA model. International Journal of Engineering Business Management. 2018; 10. DOI: https://doi.org/10.1177/1847979018808
[20] Do P, Chow C W, Rameezdeen R, & Gorjian N. Wastewater inflow time series forecasting at low temporal resolution using SARIMA model: a case study in South Australia. Environmental Science and Pollution Research. 2022; 29(47), 70984-70999. DOI: https://doi.org/10.1007/s11356-022-20777-y
Journal of sustainable Energy Systems
Volume 5, Issue 1
January 2026
Pages 1-18

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