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Use of artificial neural networks for transport energy demand modeling

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  1. Ceylan, Huseyin & Ceylan, Halim & Haldenbilen, Soner & Baskan, Ozgur, 2008. "Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2527-2535, July.
  2. Sun, Mei & Wang, Xiaofang & Chen, Ying & Tian, Lixin, 2011. "Energy resources demand-supply system analysis and empirical research based on non-linear approach," Energy, Elsevier, vol. 36(9), pages 5460-5465.
  3. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
  4. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
  5. Adom, Philip Kofi & Amakye, Kwaku & Barnor, Charles & Quartey, George & Bekoe, William, 2016. "Shift in demand elasticities, road energy forecast and the persistence profile of shocks," Economic Modelling, Elsevier, vol. 55(C), pages 189-206.
  6. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
  7. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
  8. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  9. 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.
  10. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
  11. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
  12. 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.
  13. Yang, Honglin & Wang, Lin & Tian, Lixin, 2015. "Evolution of competition in energy alternative pathway and the influence of energy policy on economic growth," Energy, Elsevier, vol. 88(C), pages 223-233.
  14. Xiaobing Yu, 2017. "Disaster prediction model based on support vector machine for regression and improved differential evolution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 959-976, January.
  15. Fang, Guochang & Tian, Lixin & Sun, Mei & Fu, Min, 2012. "Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system," Energy, Elsevier, vol. 40(1), pages 291-299.
  16. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  17. 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.
  18. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
  19. 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).
  20. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
  21. Manuel Llorca & Jose Banos & Somoza Jose & Pelayo Arbues, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, , vol. 38(5), pages 153-174, September.
  22. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
  23. 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.
  24. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  25. 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.
  26. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
  27. Sharifi, Mehdi & Khazaei Pool, Javad & Jalilvand, Mohammad Reza & Tabaeeian, Reihaneh Alsadat & Ghanbarpour Jooybari, Mohsen, 2019. "Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 154-161.
  28. 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.
  29. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
  30. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  31. 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.
  32. Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
  33. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2014. "Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system," Energy Policy, Elsevier, vol. 68(C), pages 498-507.
  34. Zhao, Jingjing & Heydari, Shahram & Forrest, Michael & Stevens, Alan & Preston, John, 2023. "Investigating correlates of personal and freight road transport energy consumption: A case study of England," Journal of Transport Geography, Elsevier, vol. 112(C).
  35. Li, Zheng & Rose, John M. & Hensher, David A., 2010. "Forecasting automobile petrol demand in Australia: An evaluation of empirical models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(1), pages 16-38, January.
  36. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
  37. 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).
  38. 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.
  39. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  40. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
  41. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
  42. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
  43. 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.
  44. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
  45. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
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