Open Access

REVIEW

AI-Powered Digital Twin Frameworks for Smart Grid Optimization and Real-Time Energy Management in Smart Buildings: A Survey

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

1 Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
2 Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77004, USA
3 College of Arts and Letters, School of Art, Florida Atlantic University, Boca Raton, FL 33431, USA
4 Faculty of Industrial Engineering, Urmia University of Technology, Urmia, 57169-31557, Iran
5 Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33174, USA
6 Department of Electrical and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA

* Corresponding Author: Mohsen Ahmadi. Email:

(This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)

Computer Modeling in Engineering & Sciences 2025, 145(2), 1259-1301. https://doi.org/10.32604/cmes.2025.070528

Received 18 July 2025; Accepted 30 September 2025; Issue published 26 November 2025

Abstract

The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control. Through this synergy, DT–ML systems enhance energy prediction, occupant comfort, and automated fault detection, while also supporting broader sustainability goals. The review examines recent advances in DT–ML energy systems, with attention to enabling technologies such as IoT sensor networks, building energy management systems, edge–cloud computing, and advanced analytics. Key challenges including data interoperability, cybersecurity, scalability, and the need for standardized frameworks are critically discussed, along with emerging solutions such as federated learning and blockchain. Special focus is given to human-centric digital twin frameworks that integrate user comfort and behavioral adaptation into energy optimization strategies. The findings suggest that DT–ML integration, enabled by IoT sensor networks, has the potential to significantly reduce energy consumption, lower operational costs, and improve resilience in urban infrastructures. The paper concludes by outlining future research priorities, including decentralized learning models, universal data standards, enhanced privacy protocols, and expanding digital twin applications for distributed renewable energy resources.

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Keywords

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