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Digital Twins Concept For Energy-Efficient Smart Buildings

Author

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  • Mihail Mateev

    (Faculty of Structural Engineering, University of Architecture, Civil Engineering and Geodesy, Sofia)

Abstract

Over the past few decades, technology and inventions have fueled the development of intelligent buildings and green energy. Although digital twins have existed since the beginning of the 21st century, the rapid rise of the Internet of Things (IoT) and domains around it, like intelligent buildings, have remained more widely considered a future technology. Digital twins are getting attention because they integrate concepts like artificial intelligence (AI) and machine learning (ML) to combine data, algorithms, and context. Integrating intelligent energy-efficient buildings, information technology, and innovative development are meeting new opportunities and challenges. This review aims to analyze the digital evolution of smart buildings to make it easier to create contiguous ecological development areas in green environmental cities. All these areas are a considerable step in integrating a Digital Twin — a virtual representation of real-world constructions and even complete vertical solutions for buildings (including infrastructure, ground foundation, and equipment). It can be closely examined and examined in detail. This study discusses use cases for IoT solutions in the building sector and intelligent energy powered by digital twins, as well as best practices for their design.. The author offers a reference architecture and prototypes demonstrating the application of Digital Twins in intelligent buildings as a result of this study. This research is technology agnostic, but it offers prototypes of sample solutions concerning Microsoft Azure (Azure solutions, which include Azure Digital Twins Service and Azure IoT Stack).

Suggested Citation

  • Mihail Mateev, 2024. "Digital Twins Concept For Energy-Efficient Smart Buildings," Yearbook of the Faculty of Economics and Business Administration, Sofia University, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria, vol. 23(1), pages 187-198, June.
    • Handle: RePEc:sko:yrbook:v:23:y:2024:i:1:p:187-198
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    References listed on IDEAS

    as
    1. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
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