AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges

Adel Mounir Said; E. Southon; M. Marot; H. Afifi; Hassine Moungla

doi:10.1109/ICCE67443.2026.11449731

AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges

Adel Mounir Said
, E. Southon
, M. Marot
, H. Afifi
, Hassine Moungla

Switching Department
Telecom Sudparis
Computer and Systems Department

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

1 Citation (Scopus)

Résumé

Artificial Intelligence-driven Digital Twins (DTs) for energy management offer valuable insights for stakeholders by enabling real-time monitoring, prediction, and optimization of energy systems. This paper first clarifies the DT concept through a concise survey of its different interpretations and applications. As a practical implementation, the ORION EU H2020 project is presented, with its objectives mapped onto a DT-based architecture for sustainable energy management. Key challenges - such as data heterogeneity, scalability, and privacy constraints - are analyzed, and potential solutions are proposed for different use cases. Finally, experimental results from prototype implementations are discussed, demonstrating the feasibility and benefits of AI-enabled DTs in improving energy efficiency and decision-making.

langue originale	Anglais
titre	2026 IEEE International Conference on Consumer Electronics, ICCE 2026
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9798331553432
Les DOIs	https://doi.org/10.1109/ICCE67443.2026.11449731
état	Publié - 1 janv. 2026
Evénement	2026 IEEE International Conference on Consumer Electronics, ICCE 2026 - Dubai, Émirats arabes unis Durée: 3 févr. 2026 → 5 févr. 2026

Série de publications

Nom	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (imprimé)	0747-668X
ISSN (Electronique)	2159-1423

Une conférence

Une conférence	2026 IEEE International Conference on Consumer Electronics, ICCE 2026
Pays/Territoire	Émirats arabes unis
La ville	Dubai
période	3/02/26 → 5/02/26

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre

Accès au document

10.1109/ICCE67443.2026.11449731

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Contient cette citation

Said, A. M., Southon, E., Marot, M., Afifi, H., & Moungla, H. (2026). AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges. Dans 2026 IEEE International Conference on Consumer Electronics, ICCE 2026 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE67443.2026.11449731

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title = "AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges",

abstract = "Artificial Intelligence-driven Digital Twins (DTs) for energy management offer valuable insights for stakeholders by enabling real-time monitoring, prediction, and optimization of energy systems. This paper first clarifies the DT concept through a concise survey of its different interpretations and applications. As a practical implementation, the ORION EU H2020 project is presented, with its objectives mapped onto a DT-based architecture for sustainable energy management. Key challenges - such as data heterogeneity, scalability, and privacy constraints - are analyzed, and potential solutions are proposed for different use cases. Finally, experimental results from prototype implementations are discussed, demonstrating the feasibility and benefits of AI-enabled DTs in improving energy efficiency and decision-making.",

keywords = "Artificial Intelligence, Digital Twin, Energy, Machine Learning",

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booktitle = "2026 IEEE International Conference on Consumer Electronics, ICCE 2026",

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Said, AM, Southon, E, Marot, M , Afifi, H & Moungla, H 2026, AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges. Dans 2026 IEEE International Conference on Consumer Electronics, ICCE 2026. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, Institute of Electrical and Electronics Engineers Inc., 2026 IEEE International Conference on Consumer Electronics, ICCE 2026, Dubai, Émirats arabes unis, 3/02/26. https://doi.org/10.1109/ICCE67443.2026.11449731

AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges. / Said, Adel Mounir; Southon, E.; Marot, M. et al.
2026 IEEE International Conference on Consumer Electronics, ICCE 2026. Institute of Electrical and Electronics Engineers Inc., 2026. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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T1 - AI Assisted Digital Twins for Energy Management Systems

T2 - 2026 IEEE International Conference on Consumer Electronics, ICCE 2026

AU - Said, Adel Mounir

AU - Southon, E.

AU - Marot, M.

AU - Afifi, H.

AU - Moungla, Hassine

PY - 2026/1/1

Y1 - 2026/1/1

N2 - Artificial Intelligence-driven Digital Twins (DTs) for energy management offer valuable insights for stakeholders by enabling real-time monitoring, prediction, and optimization of energy systems. This paper first clarifies the DT concept through a concise survey of its different interpretations and applications. As a practical implementation, the ORION EU H2020 project is presented, with its objectives mapped onto a DT-based architecture for sustainable energy management. Key challenges - such as data heterogeneity, scalability, and privacy constraints - are analyzed, and potential solutions are proposed for different use cases. Finally, experimental results from prototype implementations are discussed, demonstrating the feasibility and benefits of AI-enabled DTs in improving energy efficiency and decision-making.

AB - Artificial Intelligence-driven Digital Twins (DTs) for energy management offer valuable insights for stakeholders by enabling real-time monitoring, prediction, and optimization of energy systems. This paper first clarifies the DT concept through a concise survey of its different interpretations and applications. As a practical implementation, the ORION EU H2020 project is presented, with its objectives mapped onto a DT-based architecture for sustainable energy management. Key challenges - such as data heterogeneity, scalability, and privacy constraints - are analyzed, and potential solutions are proposed for different use cases. Finally, experimental results from prototype implementations are discussed, demonstrating the feasibility and benefits of AI-enabled DTs in improving energy efficiency and decision-making.

KW - Artificial Intelligence

KW - Digital Twin

KW - Energy

KW - Machine Learning

UR - https://www.scopus.com/pages/publications/105037390648

U2 - 10.1109/ICCE67443.2026.11449731

DO - 10.1109/ICCE67443.2026.11449731

M3 - Conference contribution

AN - SCOPUS:105037390648

T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics

BT - 2026 IEEE International Conference on Consumer Electronics, ICCE 2026

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 3 February 2026 through 5 February 2026

ER -

Said AM, Southon E, Marot M , Afifi H, Moungla H. AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges. Dans 2026 IEEE International Conference on Consumer Electronics, ICCE 2026. Institute of Electrical and Electronics Engineers Inc. 2026. (Digest of Technical Papers - IEEE International Conference on Consumer Electronics). doi: 10.1109/ICCE67443.2026.11449731

AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges

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