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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

What the paper says

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.