Hybrid Digital Twin and IoT Framework for Smart Building Energy Optimization

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2025PI2F6Y

    Published 04-05-2025

  • Digital Twin, Internet Of Things (Iot), Smart Buildings, Building Energy Management System (BEMS), Energy Optimization, Artificial Intelligence, Machine Learning, Cloud Computing, Edge Computing, Cyber-Physical Systems, Predictive Analytics, HVAC Optimization, Sustainable Buildings, Intelligent Energy Management, Smart Cities

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    Hybrid Digital Twin and IoT Framework for Smart Building Energy Optimization. (2025). International Journal of Modern Research in Science & Engineering, 8(1), 01-13. https://doi.org/10.67228/30716357/IJMRSE-2025PI2F6Y

  • Abstract

    Rapid urbanization, increasing energy demand, and stringent environmental regulations have accelerated the adoption of smart building management systems. However, conventional Building Energy Management Systems (BEMS) rely on static control strategies and limited predictive capabilities, resulting in inefficient energy utilization. This paper proposes a Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization. Environmental and operational data from sensors, including temperature, humidity, occupancy, lighting, CO₂ concentration, and energy meters, are processed at the edge and synchronized with a cloud-based Digital Twin for real-time monitoring and predictive analytics. The framework forecasts energy demand, occupant behavior, and HVAC performance while optimizing building operations to reduce energy consumption and operational costs without compromising occupant comfort. Continuous interaction between the physical building and its virtual counterpart enables predictive maintenance, adaptive control, intelligent fault diagnosis, and renewable energy integration, delivering scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making.

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