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Transforming Energy Management with an AI-Enabled Digital Twin

Hadi Ghanbari & Petter Nissinen

MIS Quarterly Executive2025https://doi.org/10.17705/2msqe.00120article
AJG 2ABDC A
Weight
0.37

Abstract

Digital twins (DTs) are increasingly adopted by organizations across various sectors. We report on how one of Europe’s largest district heating providers implemented an AI-assisted DT in pursuit of energy efficiency and sustainability. The solution enabled the company to modernize its complex cyber-physical system (CPS) and tap into its rich data capabilities to gain a comprehensive real-time representation of the entire district heating network. Reflecting on the case study, we provide six recommendations for executives in other domains aiming to implement DTs.

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https://doi.org/https://doi.org/10.17705/2msqe.00120

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@article{hadi2025,
  title        = {{Transforming Energy Management with an AI-Enabled Digital Twin}},
  author       = {Hadi Ghanbari & Petter Nissinen},
  journal      = {MIS Quarterly Executive},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/2msqe.00120},
}

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Transforming Energy Management with an AI-Enabled Digital Twin

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.