IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v249y2022ics0360544222006387.html
   My bibliography  Save this article

Prediction of transportation energy demand by novel hybrid meta-heuristic ANN

Author

Listed:
  • Sahraei, Mohammad Ali
  • Çodur, Merve Kayaci

Abstract

Road automobiles are deemed one of the major resources of energy consumption throughout cities. To realize and design sustainable urban transport, it is essential to comprehend as well as evaluate interactions among a set of elements, which form transport impacts and behaviors. The goal of the current research was to propose a hybrid algorithm, Artificial Neural Network (ANN)-Genetic Algorithm (ANN-GA), ANN-Simulated Annealing (ANN-SA), and Particle Swarm Optimization (ANN-PSO) to better optimize the coefficients for predicting the energy demand based on the several predictor variables (1975–2019) i.e., GDP, year, vehicle-km, population, oil price, passenger-km, and ton-km in Turkey. Eleven combinations of all predictor variables were selected and then compared with real data. The outcomes exposed that the proposed ANN-PSO technique based on the GDP, population, ton-km outperforms the other two models. It is anticipated that this research can be useful for developing extremely productive and applicable planning regarding transportation energy policies.

Suggested Citation

  • Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
  • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006387
    DOI: 10.1016/j.energy.2022.123735
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222006387
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.123735?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. 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.
    2. Peng, Tianduo & Ou, Xunmin & Yuan, Zhiyi & Yan, Xiaoyu & Zhang, Xiliang, 2018. "Development and application of China provincial road transport energy demand and GHG emissions analysis model," Applied Energy, Elsevier, vol. 222(C), pages 313-328.
    3. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    4. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    5. Deendarlianto, & Widyaparaga, Adhika & Sopha, Bertha Maya & Budiman, Arief & Muthohar, Imam & Setiawan, Indra Chandra & Lindasista, Alia & Soemardjito, Joewono & Oka, Kazutaka, 2017. "Scenarios analysis of energy mix for road transportation sector in Indonesia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 13-23.
    6. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    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. 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.
    9. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    10. 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.
    11. Ishibuchi, Hisao & Misaki, Shinta & Tanaka, Hideo, 1995. "Modified simulated annealing algorithms for the flow shop sequencing problem," European Journal of Operational Research, Elsevier, vol. 81(2), pages 388-398, March.
    12. 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.
    13. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    14. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
    15. 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.
    16. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    17. Utgikar, V.P. & Scott, J.P., 2006. "Energy forecasting: Predictions, reality and analysis of causes of error," Energy Policy, Elsevier, vol. 34(17), pages 3087-3092, November.
    18. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    19. Haldenbilen, Soner & Ceylan, Halim, 2005. "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, Elsevier, vol. 33(1), pages 89-98, January.
    20. Muhammad Ali Mughal & Qishuang Ma & Chunyan Xiao, 2017. "Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing," Energies, MDPI, vol. 10(8), pages 1-14, August.
    21. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    22. Zhang, Weiping & Maleki, Akbar & Rosen, Marc A. & Liu, Jingqing, 2018. "Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage," Energy, Elsevier, vol. 163(C), pages 191-207.
    23. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
    24. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    25. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    26. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    27. Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    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. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    2. Mohammad Talaei & Majid Astaneh & Elmira Ghiasabadi Farahani & Farzin Golzar, 2023. "Application of Artificial Intelligence for Predicting CO 2 Emission Using Weighted Multi-Task Learning," Energies, MDPI, vol. 16(16), pages 1-18, August.
    3. Xu, Qilong & Wang, Shuai & Luo, Kun & Mu, Yanfei & Pan, Lu & Fan, Jianren, 2023. "Process modelling and optimization of a 250 MW IGCC system: ASU optimization and thermodynamic analysis," Energy, Elsevier, vol. 282(C).
    4. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    5. Ahmadi, Pouria & Raeesi, Mehrdad & Changizian, Sina & Teimouri, Aidin & Khoshnevisan, Alireza, 2022. "Lifecycle assessment of diesel, diesel-electric and hydrogen fuel cell transit buses with fuel cell degradation and battery aging using machine learning techniques," Energy, Elsevier, vol. 259(C).
    6. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    7. Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Abdullah G. Alharbi & Sulaiman Alshammari & Yahia B. Hassan, 2023. "Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    8. Liu, Bingchun & Song, Jiangji & Wang, Qingshan & Xu, Yan & Liu, Yifan, 2023. "Charging station forecasting and scenario analysis in China," Transport Policy, Elsevier, vol. 139(C), pages 87-98.
    9. Hu, Changshuai & Du, Dan & Huang, Junbing, 2023. "The driving effect of energy demand evolution: From the perspective of heterogeneity in technology," Energy, Elsevier, vol. 275(C).

    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. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    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. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    4. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    5. 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).
    6. 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.
    7. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    8. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    9. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    10. 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.
    11. Abdulkerim Karaaslan & Mesliha Gezen, 2017. "Forecasting of Turkey s Sectoral Energy Demand by Using Fuzzy Grey Regression Model," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 67-77.
    12. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    13. 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.
    14. 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.
    15. 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.
    16. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    17. Colmenar, J.M. & Hidalgo, J.I. & Salcedo-Sanz, S., 2018. "Automatic generation of models for energy demand estimation using Grammatical Evolution," Energy, Elsevier, vol. 164(C), pages 183-193.
    18. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    19. 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.
    20. Huang, Zishuo & Yu, Hang & Peng, Zhenwei & Zhao, Mei, 2015. "Methods and tools for community energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1335-1348.

    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:249:y:2022:i:c:s0360544222006387. 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.