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Explainable Dual-Attention Encoder–Decoder Model for Natural Gas Consumption Forecasting Using Algerian Hourly Data

In: Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

Author

Listed:
  • Randa Ladlani

    (École supérieure en Sciences et Technologies de l’Informatique et du Numérique, Laboratoire LITAN)

  • Samiha Ait Taleb

    (École supérieure en Sciences et Technologies de l’Informatique et du Numérique, Laboratoire LITAN
    University of Bejaia, LIMED Laboratory, Computer Science Department)

  • Abderrazak Sebaa

    (École supérieure en Sciences et Technologies de l’Informatique et du Numérique, Laboratoire LITAN
    University of Bejaia, LIMED Laboratory, Computer Science Department)

Abstract

Natural gas consumption forecasting supports efficient energy management and resource planning in industrial environments. The proposed architecture combines an encoder–decoder structure with dual attention mechanisms — temporal and feature-level — alongside cyclical encodings and moving-average representations to improve forecasting accuracy and stability. Evaluation on Algerian hourly natural gas data yields Test MAE = 0.0255 and R2 = 0.9740, outperforming classical machine learning and deep learning baselines by up to 38%. Cross-domain validation on the GEFCom2014 electricity load benchmark confirms generalizability (R2 = 0.9817, 67% MAE reduction over XGBoost). SHAP analysis quantifies feature contributions, identifying historical consumption and temporal encodings as the dominant predictors, with meteorological variables providing secondary refinement.

Suggested Citation

  • Randa Ladlani & Samiha Ait Taleb & Abderrazak Sebaa, 2026. "Explainable Dual-Attention Encoder–Decoder Model for Natural Gas Consumption Forecasting Using Algerian Hourly Data," Advances in Economics, Business and Management Research, in: Djouhara Agti & Salim Bitam & Fateh Debla & Reguia Cherroun (ed.), Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026), pages 345-355, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-711-8_32
    DOI: 10.2991/978-94-6239-711-8_32
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