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<p>Accurate forecasting of energy consumption has emerged as a critical requirement in the evolution of sustainable and intelligent transportation systems. This also helps in reduction of fuel costs thus causing lower carbon emission and optimal vehicle performance. Existing studies present various machine learning and deep learning models considering various features however lack to use state of art transformers. This study considers the features sets of operational and environmental using Feature Tokenizer Transformer (FT-Transformer). The proposed model considers feature tokenizer to learn both feature sets using self-attention mechanism. The approach interprets various machine learning methods with advanced neural architecture. The empirical analysis demonstrates that proposed model achieves the highest predictive results with lowest mean absolute error of 0.16, root means square of 0.21 and with R² value of 0.99 as compared to latest existing models in the relevant studies. In addition, we apply XAI based techniques which describes how the proposed model generate outputs helping to understand the factors influencing predictions and decisions. XAI methods of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) presents the significance of features and their role in overall prediction.</p>

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Energy consumption forecasting in logistics considering environmental and operational constraints using FT-transformer architecture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Lai Yan

摘要

Accurate forecasting of energy consumption has emerged as a critical requirement in the evolution of sustainable and intelligent transportation systems. This also helps in reduction of fuel costs thus causing lower carbon emission and optimal vehicle performance. Existing studies present various machine learning and deep learning models considering various features however lack to use state of art transformers. This study considers the features sets of operational and environmental using Feature Tokenizer Transformer (FT-Transformer). The proposed model considers feature tokenizer to learn both feature sets using self-attention mechanism. The approach interprets various machine learning methods with advanced neural architecture. The empirical analysis demonstrates that proposed model achieves the highest predictive results with lowest mean absolute error of 0.16, root means square of 0.21 and with R² value of 0.99 as compared to latest existing models in the relevant studies. In addition, we apply XAI based techniques which describes how the proposed model generate outputs helping to understand the factors influencing predictions and decisions. XAI methods of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) presents the significance of features and their role in overall prediction.