IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v36y2011i9p5649-5654.html
   My bibliography  Save this article

Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)

Author

Listed:
  • Behrang, M.A.
  • Assareh, E.
  • Ghalambaz, M.
  • Assari, M.R.
  • Noghrehabadi, A.R.

Abstract

Growing energy demand of the world, made the major oil and gas exporting countries to have critical role in the energy supply. The geostrategic situation of Iran and its access to the huge hydrocarbon resources placed the country among important areas and resulted in the investment development of oil and gas industry.

Suggested Citation

  • Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:9:p:5649-5654
    DOI: 10.1016/j.energy.2011.07.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211004506
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2011.07.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    2. Henning, Dag, 1997. "MODEST—An energy-system optimisation model applicable to local utilities and countries," Energy, Elsevier, vol. 22(12), pages 1135-1150.
    3. Rout, Ullash K. & Voβ, Alfred & Singh, Anoop & Fahl, Ulrich & Blesl, Markus & Ó Gallachóir, Brian P., 2011. "Energy and emissions forecast of China over a long-time horizon," Energy, Elsevier, vol. 36(1), pages 1-11.
    4. Groscurth, H.-M. & Kress, K.-P., 1998. "Fuzzy data compression for energy optimization models," Energy, Elsevier, vol. 23(1), pages 1-9.
    5. Partovi, Farzad & Nikzad, Mehdi & Mozafari, Babak & Ranjbar, Ali Mohamad, 2011. "A stochastic security approach to energy and spinning reserve scheduling considering demand response program," Energy, Elsevier, vol. 36(5), pages 3130-3137.
    6. Wright, Evelyn L. & Belt, Juan A.B. & Chambers, Adam & Delaquil, Pat & Goldstein, Gary, 2010. "A scenario analysis of investment options for the Cuban power sector using the MARKAL model," Energy Policy, Elsevier, vol. 38(7), pages 3342-3355, July.
    7. Ünler, Alper, 2008. "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, Elsevier, vol. 36(6), pages 1937-1944, June.
    8. Fulton, Lew & Cazzola, Pierpaolo & Cuenot, François, 2009. "IEA Mobility Model (MoMo) and its use in the ETP 2008," Energy Policy, Elsevier, vol. 37(10), pages 3758-3768, October.
    9. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    10. Canyurt, Olcay Ersel & Ozturk, Harun Kemal, 2008. "Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2562-2569, July.
    11. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    12. Askari, Hossein & Krichene, Noureddine, 2010. "An oil demand and supply model incorporating monetary policy," Energy, Elsevier, vol. 35(5), pages 2013-2021.
    13. Breukers, S.C. & Heiskanen, E. & Brohmann, B. & Mourik, R.M. & Feenstra, C.F.J., 2011. "Connecting research to practice to improve energy demand-side management (DSM)," Energy, Elsevier, vol. 36(4), pages 2176-2185.
    14. Ito, Toshihide & Chen, Youqing & Ito, Shoichi & Yamaguchi, Kaoru, 2010. "Prospect of the upper limit of the energy demand in China from regional aspects," Energy, Elsevier, vol. 35(12), pages 5320-5327.
    15. Tang, Xu & Zhang, Baosheng & Höök, Mikael & Feng, Lianyong, 2010. "Forecast of oil reserves and production in Daqing oilfield of China," Energy, Elsevier, vol. 35(7), pages 3097-3102.
    16. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    17. Cinar, Didem & Kayakutlu, Gulgun & Daim, Tugrul, 2010. "Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey," Energy, Elsevier, vol. 35(4), pages 1724-1729.
    18. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    19. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    20. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    21. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    22. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    23. Lin, Q.G. & Huang, G.H. & Bass, B. & Qin, X.S., 2009. "IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty," Energy Policy, Elsevier, vol. 37(3), pages 868-878, March.
    24. Duran Toksari, M., 2007. "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, Elsevier, vol. 35(8), pages 3984-3990, August.
    25. Hainoun, A. & Seif-Eldin, M.K. & Almoustafa, S., 2006. "Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology," Energy Policy, Elsevier, vol. 34(14), pages 1958-1970, September.
    26. Greene, David L. & Hopson, Janet L. & Li, Jia, 2006. "Have we run out of oil yet? Oil peaking analysis from an optimist's perspective," Energy Policy, Elsevier, vol. 34(5), pages 515-531, March.
    27. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    28. Finon, Dominique, 1974. "Optimisation model for the French energy sector," Energy Policy, Elsevier, vol. 2(2), pages 136-151, June.
    29. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    30. Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2010. "Modelling energy demand of developing countries: Are the specific features adequately captured?," Energy Policy, Elsevier, vol. 38(4), pages 1979-1990, April.
    31. Karbassi, A.R. & Abduli, M.A. & Mahin Abdollahzadeh, E., 2007. "Sustainability of energy production and use in Iran," Energy Policy, Elsevier, vol. 35(10), pages 5171-5180, October.
    32. Aslan, Alper & Kum, Hakan, 2011. "The stationary of energy consumption for Turkish disaggregate data by employing linear and nonlinear unit root tests," Energy, Elsevier, vol. 36(7), pages 4256-4258.
    33. Jovanovic, Marina & Afgan, Naim & Bakic, Vukman, 2010. "An analytical method for the measurement of energy system sustainability in urban areas," Energy, Elsevier, vol. 35(9), pages 3909-3920.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tatiana Mitrova & Vyacheslav Kulagin & Dmitry Grushevenko & Ekaterina Grushevenko, 2015. "Technological Innovation as a Factor of Demand for Energy Sources in Automotive Industry," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 9(4), pages 18-31.
    2. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    3. Ekaterina Grushevenko, 2015. "Complex method of petroleum products demand forecasting considering economic, demographic and technological factors," Economics and Business Letters, Oviedo University Press, vol. 4(3), pages 98-107.
    4. Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    5. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
    6. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    7. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    8. Anupam Biswas & K. K. Mishra & Shailesh Tiwari & A. K. Misra, 2013. "Physics-Inspired Optimization Algorithms: A Survey," Journal of Optimization, Hindawi, vol. 2013, pages 1-16, June.
    9. Ji, Bin & Yuan, Xiaohui & Chen, Zhihuan & Tian, Hao, 2014. "Improved gravitational search algorithm for unit commitment considering uncertainty of wind power," Energy, Elsevier, vol. 67(C), pages 52-62.
    10. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    11. Sarita Gajbhiye Meshram & M. A. Ghorbani & Ravinesh C. Deo & Mahsa Hasanpour Kashani & Chandrashekhar Meshram & Vahid Karimi, 2019. "New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2335-2356, May.
    12. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.
    13. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    4. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    5. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    6. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    8. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    9. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    10. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    11. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    12. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    13. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    14. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.
    15. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    16. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    17. Pukšec, Tomislav & Mathiesen, Brian Vad & Novosel, Tomislav & Duić, Neven, 2014. "Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia," Energy, Elsevier, vol. 76(C), pages 198-209.
    18. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    19. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    20. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:36:y:2011:i:9:p:5649-5654. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.