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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2026 IEEE International Conference on Consumer Electronics, ICCE 2026
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331553432
DOIs	https://doi.org/10.1109/ICCE67443.2026.11449731
Publication status	Published - 1 Jan 2026
Event	2026 IEEE International Conference on Consumer Electronics, ICCE 2026 - Dubai, United Arab Emirates Duration: 3 Feb 2026 → 5 Feb 2026

Publication series

Name	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)	0747-668X
ISSN (Electronic)	2159-1423

Conference

Conference	2026 IEEE International Conference on Consumer Electronics, ICCE 2026
Country/Territory	United Arab Emirates
City	Dubai
Period	3/02/26 → 5/02/26

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial Intelligence
Digital Twin
Energy
Machine Learning

Access to Document

10.1109/ICCE67443.2026.11449731

Cite this

Said, A. M., Southon, E., Marot, M., Afifi, H., & Moungla, H. (2026). AI Assisted Digital Twins for Energy Management Systems: Survey and Challenges. In 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",

author = "Said, \{Adel Mounir\} and E. Southon and M. Marot and H. Afifi and Hassine Moungla",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 IEEE International Conference on Consumer Electronics, ICCE 2026 ; Conference date: 03-02-2026 Through 05-02-2026",

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doi = "10.1109/ICCE67443.2026.11449731",

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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. in 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, United Arab Emirates, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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