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

International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

📍Biskra, Algeria🗓️ 13-14 April 2026
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Explainable Dual-Attention Encoder–Decoder Model for Natural Gas Consumption Forecasting Using Algerian Hourly Data

Authors
Randa Ladlani1, *, Samiha Ait Taleb1, 2, Abderrazak Sebaa1, 2
1Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numérique, RN 75, Amizour, 06300, Bejaia, Algeria
2LIMED Laboratory, Computer Science Department, University of Bejaia, 06000, Bejaia, Algeria
*Corresponding author. Email: ladlani@estin.dz
Corresponding Author
Randa Ladlani
Available Online 24 June 2026.
DOI
10.2991/978-94-6239-711-8_32How to use a DOI?
Keywords
Natural Gas Consumption Forecasting; Dual Attention Mechanism; Encoder-Decoder Architecture; Explainable AI; SHAP
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.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
Series
Advances in Economics, Business and Management Research
Publication Date
24 June 2026
ISBN
978-94-6239-711-8
ISSN
2352-5428
DOI
10.2991/978-94-6239-711-8_32How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Randa Ladlani
AU  - Samiha Ait Taleb
AU  - Abderrazak Sebaa
PY  - 2026
DA  - 2026/06/24
TI  - Explainable Dual-Attention Encoder–Decoder Model for Natural Gas Consumption Forecasting Using Algerian Hourly Data
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 345
EP  - 355
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_32
DO  - 10.2991/978-94-6239-711-8_32
ID  - Ladlani2026
ER  -