IDEAS home Printed from https://ideas.repec.org/r/eee/enepol/v39y2011i5p2754-2763.html
   My bibliography  Save this item

Projection of future transport energy demand of Thailand

Citations

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


Cited by:

  1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  2. 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.
  3. 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.
  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. Llorca, Manuel & Jamasb, Tooraj, 2017. "Energy efficiency and rebound effect in European road freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 98-110.
  6. 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.
  7. 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).
  8. Hana Kim, 2018. "Economic and environmental implications of the recent energy transition on South Korea’s electricity sector," Energy & Environment, , vol. 29(5), pages 752-769, August.
  9. 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.
  10. 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.
  11. Shiwen Liu & Hongxiong Li & Wen Kun & Zhen Zhang & Haotian Wu, 2022. "How Do Transportation Influencing Factors Affect Air Pollutants from Vehicles in China? Evidence from Threshold Effect," Sustainability, MDPI, vol. 14(15), pages 1-15, August.
  12. Alipour, M. & Hafezi, R. & Amer, M. & Akhavan, A.N., 2017. "A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post–sanction period," Energy, Elsevier, vol. 135(C), pages 851-864.
  13. 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).
  14. Selvakkumaran, Sujeetha & Limmeechokchai, Bundit, 2015. "Low carbon society scenario analysis of transport sector of an emerging economy—The AIM/Enduse modelling approach," Energy Policy, Elsevier, vol. 81(C), pages 199-214.
  15. Ülengin, Füsun & Işık, Mine & Ekici, Şule Önsel & Özaydın, Özay & Kabak, Özgür & Topçu, Y. İlker, 2018. "Policy developments for the reduction of climate change impacts by the transportation sector," Transport Policy, Elsevier, vol. 61(C), pages 36-50.
  16. Navas-Anguita, Zaira & García-Gusano, Diego & Iribarren, Diego, 2019. "A review of techno-economic data for road transportation fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 11-26.
  17. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  18. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
  19. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  20. 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).
  21. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
  22. 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.
  23. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
  24. Monica Maduekwe & Uduak Akpan & Salisu Isihak, 2020. "Road Transport Energy Consumption and Vehicular Emissions in Lagos, Nigeria," Working Papers 20/055, European Xtramile Centre of African Studies (EXCAS).
  25. 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.
  26. Chollacoop, Nuwong & Saisirirat, Peerawat & Sukkasi, Sittha & Tongroon, Manida & Fukuda, Tuenjai & Fukuda, Atsushi & Nivitchanyong, Siriluck, 2013. "Potential of greenhouse gas emission reduction in Thai road transport by ethanol bus technology," Applied Energy, Elsevier, vol. 102(C), pages 112-123.
  27. 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.
  28. 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.
  29. 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.
  30. Prasad, Ravita D. & Bansal, R.C. & Raturi, Atul, 2014. "Multi-faceted energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 686-699.
  31. Maaouane, Mohamed & Zouggar, Smail & Krajačić, Goran & Zahboune, Hassan, 2021. "Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods," Energy, Elsevier, vol. 225(C).
  32. 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).
  33. 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.
  34. 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.
  35. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & Wood, David A., 2019. "A Layered Uncertainties Scenario Synthesizing (LUSS) model applied to evaluate multiple potential long-run outcomes for Iran's natural gas exports," Energy, Elsevier, vol. 169(C), pages 646-659.
  36. 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).
  37. Dioha, Michael O. & Kumar, Atul, 2020. "Sustainable energy pathways for land transport in Nigeria," Utilities Policy, Elsevier, vol. 64(C).
  38. 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.
  39. 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.
  40. 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).
  41. 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.
  42. 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.
  43. Daniel Wojtkowiak & Piotr Cyplik, 2020. "Operational Excellence within Sustainable Development Concept-Systematic Literature Review," Sustainability, MDPI, vol. 12(19), pages 1-13, September.
  44. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  45. Hualong Yang & Xuefei Ma & Yuwei Xing, 2017. "Trends in CO 2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications," Energies, MDPI, vol. 10(7), pages 1-17, July.
  46. Pukšec, Tomislav & Krajačić, Goran & Lulić, Zoran & Mathiesen, Brian Vad & Duić, Neven, 2013. "Forecasting long-term energy demand of Croatian transport sector," Energy, Elsevier, vol. 57(C), pages 169-176.
  47. 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.
  48. Qiao, Qingyao & Eskandari, Hamidreza & Saadatmand, Hassan & Sahraei, Mohammad Ali, 2024. "An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector," Energy, Elsevier, vol. 286(C).
  49. Sadri, A. & Ardehali, M.M. & Amirnekooei, K., 2014. "General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN," Energy, Elsevier, vol. 77(C), pages 831-843.
  50. Ratanavaraha, Vatanavongs & Jomnonkwao, Sajjakaj, 2015. "Trends in Thailand CO2 emissions in the transportation sector and Policy Mitigation," Transport Policy, Elsevier, vol. 41(C), pages 136-146.
  51. 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.
  52. Martínez-Jaramillo, Juan Esteban & Arango-Aramburo, Santiago & Álvarez-Uribe, Karla C. & Jaramillo-Álvarez, Patricia, 2017. "Assessing the impacts of transport policies through energy system simulation: The case of the Medellin Metropolitan Area, Colombia," Energy Policy, Elsevier, vol. 101(C), pages 101-108.
  53. 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.
  54. Hong, Sungjun & Chung, Yanghon & Kim, Jongwook & Chun, Dongphil, 2016. "Analysis on the level of contribution to the national greenhouse gas reduction target in Korean transportation sector using LEAP model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 549-559.
  55. Phdungsilp, Aumnad & Wuttipornpun, Teeradej, 2013. "Analyses of the decarbonizing Thailand's energy system toward low-carbon futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 187-197.
  56. Talebian, Hoda & Herrera, Omar E. & Tran, Martino & Mérida, Walter, 2018. "Electrification of road freight transport: Policy implications in British Columbia," Energy Policy, Elsevier, vol. 115(C), pages 109-118.
  57. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
  58. Kannika Duangnate & James W. Mjelde, 2022. "The Role of Pre-Commitments and Engle Curves in Thailand’s Aggregate Energy Demand System," Energies, MDPI, vol. 15(4), pages 1-16, February.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.