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Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost

Author

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  • Thanapong Champahom

    (Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Chinnakrit Banyong

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Thananya Janhuaton

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Chamroeun Se

    (Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Fareeda Watcharamaisakul

    (Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Vatanavongs Ratanavaraha

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Sajjakaj Jomnonkwao

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

Abstract

Thailand’s transport sector faces critical challenges in energy management amid rapid economic growth, with transport accounting for approximately 30% of total energy consumption. This study addresses research gaps in transport energy forecasting by comparing Long Short-Term Memory (LSTM) neural networks and XGBoost models for predicting transport energy consumption in Thailand. Utilizing a comprehensive dataset spanning 1993–2022 that includes vehicle registration data by size category, vehicle kilometers traveled, and macroeconomic indicators, this research evaluates both modeling approaches through multiple performance metrics. The results demonstrate that XGBoost consistently outperforms LSTM, achieving an R-squared value of 0.9508 for test data compared to LSTM’s 0.2005. Feature importance analysis reveals that medium vehicles contribute 36.6% to energy consumption predictions, followed by truck VKT (20.5%), with economic and demographic factors accounting for a combined 15.2%. This research contributes to both methodological understanding and practical application by establishing XGBoost’s superior performance for transport energy forecasting, quantifying the differential impact of various vehicle categories on energy consumption, and demonstrating the value of integrating vehicle registration and usage data in predictive models. The findings provide evidence-based guidance for prioritizing policy interventions in Thailand’s transport sector to enhance energy efficiency and sustainability.

Suggested Citation

  • Thanapong Champahom & Chinnakrit Banyong & Thananya Janhuaton & Chamroeun Se & Fareeda Watcharamaisakul & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2025. "Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost," Energies, MDPI, vol. 18(7), pages 1-30, March.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1685-:d:1622098
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    References listed on IDEAS

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    1. De Vos, Jonas & Alemi, Farzad, 2020. "Are young adults car-loving urbanites? Comparing young and older adults’ residential location choice, travel behavior and attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 986-998.
    2. Pongthanaisawan, Jakapong & Sorapipatana, Chumnong, 2013. "Greenhouse gas emissions from Thailand’s transport sector: Trends and mitigation options," Applied Energy, Elsevier, vol. 101(C), pages 288-298.
    3. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Canan G. Corlu & Rocio de la Torre & Adrian Serrano-Hernandez & Angel A. Juan & Javier Faulin, 2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities," Energies, MDPI, vol. 13(5), pages 1-33, March.
    5. Emami Javanmard, Majid & Tang, Yili & Martínez-Hernández, J. Adrián, 2024. "Forecasting air transportation demand and its impacts on energy consumption and emission," Applied Energy, Elsevier, vol. 364(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. 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.
    8. Ross Morrow, W. & Gallagher, Kelly Sims & Collantes, Gustavo & Lee, Henry, 2010. "Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector," Energy Policy, Elsevier, vol. 38(3), pages 1305-1320, March.
    9. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    10. Gonghao Duan & Yangwei Su & Jie Fu, 2023. "Landslide Displacement Prediction Based on Multivariate LSTM Model," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
    11. 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.
    12. Lucas Henriques & Cecilia Castro & Felipe Prata & Víctor Leiva & René Venegas, 2024. "Modeling Residential Energy Consumption Patterns with Machine Learning Methods Based on a Case Study in Brazil," Mathematics, MDPI, vol. 12(13), pages 1-33, June.
    13. 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.
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