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Forecasting the transport energy demand based on PLSR method in China

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Cited by:

  1. 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.
  2. Qodri Febrilian Erahman & Nadhilah Reyseliani & Widodo Wahyu Purwanto & Mahmud Sudibandriyo, 2019. "Modeling Future Energy Demand and CO 2 Emissions of Passenger Cars in Indonesia at the Provincial Level," Energies, MDPI, vol. 12(16), pages 1-25, August.
  3. Natallia Pashkevich & Darek Haftor & Mikael Karlsson & Soumitra Chowdhury, 2019. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
  4. 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.
  5. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
  6. Xingping Zhang & Rao Rao & Jian Xie & Yanni Liang, 2014. "The Current Dilemma and Future Path of China’s Electric Vehicles," Sustainability, MDPI, vol. 6(3), pages 1-27, March.
  7. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
  8. Yanbin Li & Zhen Li, 2019. "Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm," Energies, MDPI, vol. 12(12), pages 1-20, June.
  9. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
  10. 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.
  11. 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.
  12. 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).
  13. Weibin Lin & Bin Chen & Lina Xie & Haoran Pan, 2015. "Estimating Energy Consumption of Transport Modes in China Using DEA," Sustainability, MDPI, vol. 7(4), pages 1-15, April.
  14. 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.
  15. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
  16. 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.
  17. 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.
  18. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
  19. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
  20. Sun, Mei & Zhang, Pei-Pei & Shan, Tian-Hua & Fang, Cui-Cui & Wang, Xiao-Fang & Tian, Li-Xin, 2012. "Research on the evolution model of an energy supply–demand network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4506-4516.
  21. 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.
  22. Pan, Xunzhang & Wang, Hailin & Wang, Lining & Chen, Wenying, 2018. "Decarbonization of China's transportation sector: In light of national mitigation toward the Paris Agreement goals," Energy, Elsevier, vol. 155(C), pages 853-864.
  23. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  24. Zeng, Yuan & Tan, Xianchun & Gu, Baihe & Wang, Yi & Xu, Baoguang, 2016. "Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets," Applied Energy, Elsevier, vol. 184(C), pages 1016-1025.
  25. Yusof, Ahmad & Raman, Maznah & Nopiah, Zulkifli, 2013. "Modeling of the Malaysian Crude Oil System," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 47(1), pages 125-130.
  26. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, vol. 5(3), pages 1-22, March.
  27. 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.
  28. Keshavarzian, Maryam & Kamali Anaraki, Sara & Zamani, Mehrzad & Erfanifard, Ali, 2012. "Projections of oil demand in road transportation sector on the basis of vehicle ownership projections, worldwide: 1972–2020," Economic Modelling, Elsevier, vol. 29(5), pages 1979-1985.
  29. Wang, W.W. & Zhang, M. & Zhou, M., 2011. "Using LMDI method to analyze transport sector CO2 emissions in China," Energy, Elsevier, vol. 36(10), pages 5909-5915.
  30. 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.
  31. 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.
  32. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
  33. 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.
  34. 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.
  35. 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.
  36. Liu, Wen & Lund, Henrik & Mathiesen, Brian Vad, 2013. "Modelling the transport system in China and evaluating the current strategies towards the sustainable transport development," Energy Policy, Elsevier, vol. 58(C), pages 347-357.
  37. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 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, International Association for Energy Economics, vol. 0(Number 5).
  38. 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.
  39. Ni, Linglin & Wang, Xiaokun (Cara) & Zhang, Dapeng, 2016. "Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 12-25.
  40. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
  41. 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).
  42. Li, Peilin & Zhao, Pengjun & Brand, Christian, 2018. "Future energy use and CO2 emissions of urban passenger transport in China: A travel behavior and urban form based approach," Applied Energy, Elsevier, vol. 211(C), pages 820-842.
  43. 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).
  44. 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.
  45. 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.
  46. Jichao Geng & Ruyin Long & Hong Chen & Ting Yue & Wenbo Li & Qianwen Li, 2017. "Exploring Multiple Motivations on Urban Residents’ Travel Mode Choices: An Empirical Study from Jiangsu Province in China," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
  47. Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
  48. 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.
  49. 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.
  50. Yousaf Raza, Muhammad & Lin, Boqiang, 2022. "Natural gas consumption, energy efficiency and low carbon transition in Pakistan," Energy, Elsevier, vol. 240(C).
  51. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  52. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
  53. Fan, Ying & Xia, Yan, 2012. "Exploring energy consumption and demand in China," Energy, Elsevier, vol. 40(1), pages 23-30.
  54. 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.
  55. 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.
  56. Al-Ghandoor, A., 2013. "An approach to energy savings and improved environmental impact through restructuring Jordan's transport sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 31-42.
  57. Wang, Y.F. & Li, K.P. & Xu, X.M. & Zhang, Y.R., 2014. "Transport energy consumption and saving in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 641-655.
  58. Lin, Boqiang & Xie, Chunping, 2014. "Energy substitution effect on transport industry of China-based on trans-log production function," Energy, Elsevier, vol. 67(C), pages 213-222.
  59. Wang, H. & Zhou, P. & Zhou, D.Q., 2012. "An empirical study of direct rebound effect for passenger transport in urban China," Energy Economics, Elsevier, vol. 34(2), pages 452-460.
  60. Azadeh, A. & Saberi, M. & Asadzadeh, S.M. & Khakestani, M., 2011. "A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakis," Energy, Elsevier, vol. 36(12), pages 6981-6992.
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