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

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

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 net...

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Bibliographic Details
Main Authors: Thanapong Champahom, Chinnakrit Banyong, Thananya Janhuaton, Chamroeun Se, Fareeda Watcharamaisakul, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Energies
Subjects:
transportation energy consumption
XGBoost
LSTM
vehicle fleet composition
energy forecasting
Online Access:https://www.mdpi.com/1996-1073/18/7/1685
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