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Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Author

Listed:
  • Sofia Agostinelli

    (CITERA Interdepartmental Centre, Sapienza University of Rome, 00197 Rome, Italy)

  • Fabrizio Cumo

    (CITERA Interdepartmental Centre, Sapienza University of Rome, 00197 Rome, Italy)

  • Giambattista Guidi

    (National Agency for New Technologies, Energy and Sustainable Economic Development, 00123 Rome, Italy)

  • Claudio Tomazzoli

    (Computer Science Department, University of Verona, 37129 Verona, Italy)

Abstract

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

Suggested Citation

  • Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2338-:d:539867
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    References listed on IDEAS

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    Cited by:

    1. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    2. Roberta Moschetti & Shabnam Homaei & Ellika Taveres-Cachat & Steinar Grynning, 2022. "Assessing Responsive Building Envelope Designs through Robustness-Based Multi-Criteria Decision Making in Zero-Emission Buildings," Energies, MDPI, vol. 15(4), pages 1-27, February.
    3. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Lv, Zhihan & Cheng, Chen & Lv, Haibin, 2023. "Digital twins for secure thermal energy storage in building," Applied Energy, Elsevier, vol. 338(C).
    5. Fabrizio Cumo & Federica Giustini & Elisa Pennacchia & Carlo Romeo, 2022. "The “D2P” Approach: Digitalisation, Production and Performance in the Standardised Sustainable Deep Renovation of Buildings," Energies, MDPI, vol. 15(18), pages 1-28, September.
    6. Aleksandra Kuzior & Marek Staszek, 2021. "Energy Management in the Railway Industry: A Case Study of Rail Freight Carrier in Poland," Energies, MDPI, vol. 14(21), pages 1-21, October.
    7. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.
    8. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2021. "Open Data and Models for Energy and Environment," Energies, MDPI, vol. 14(15), pages 1-2, July.

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