Towards an efficient and interpretable Machine Learning approach for Energy Prediction in Industrial Buildings: A case study in the Steel Industry - Archive ouverte HAL
Article Dans Une Revue 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA) Année : 2022

Towards an efficient and interpretable Machine Learning approach for Energy Prediction in Industrial Buildings: A case study in the Steel Industry

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Informatique [cs]

Nourhène ben Rabah  : Connectez-vous pour contacter le contributeur

https://hal.science/hal-03971602

Soumis le : vendredi 3 février 2023-11:51:45

Dernière modification le : mercredi 27 mars 2024-15:49:16

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hal-03971602 , version 1 (03-02-2023)

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Ismehene Chahbi, Nourhène Ben Rabah, Ines Ben Tekaya. Towards an efficient and interpretable Machine Learning approach for Energy Prediction in Industrial Buildings: A case study in the Steel Industry. 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), 2022, pp.1-8. ⟨10.1109/AICCSA56895.2022.10017816⟩. ⟨hal-03971602⟩

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