The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Understanding Smart Cities: An Integrative Framework
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012) · 2012 45th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2012.615 · Cited by 2,599
About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · cited 1×
“…suitable deployment data-model based on urban intelligence systems, pervasive computing, communication, big data management technologies, and artificial intelligence (AI), leading to a strong evolution in the management of urban environments as well as in the quality of life in smart cities [7,8].”
BackgroundDigital twin-driven product design framework
Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y., & Nee, A. Y. C. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1443229 · Cited by 1,187
Internet of Things Strategic Research Roadmap
Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Jubert, I. S., Mazura, M., Harrison, M., Eisenhauer, M., et al. (2022) · Internet of Things - Global Technological and Societal Trends from Smart Environments and Spaces to Green Ict · doi:10.1201/9781003338604-2 · Cited by 1,002
The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0
Uhlemann, T. H.-J., Lehmann, C., & Steinhilper, R. (2017) · Procedia CIRP · doi:10.1016/j.procir.2016.11.152 · Cited by 994
Sensing as a service model for smart cities supported by Internet of Things
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.2704 · Cited by 991
Sacks, R., Eastman, C., Lee, G., & Teicholz, P. (2018) · Wiley eBooks · doi:10.1002/9781119287568 · Cited by 884
Smart and Digital City: A Systematic Literature Review
Cocchia, A. (2014) · Progress in IS · doi:10.1007/978-3-319-06160-3_2 · Cited by 846
Semantic web technologies in AEC industry: A literature overview
Pauwels, P., Zhang, S., & Lee, Y.-C. (2017) · Automation in Construction · doi:10.1016/j.autcon.2016.10.003 · Cited by 522
Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101692 · Cited by 484
Cited by
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.117899 · Cited by 180
Literature review of digital twin technologies for civil infrastructure
Liu, C., Zhang, P., & Xu, X. (2023) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2023.100050 · Cited by 98
Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025) · Sustainability · doi:10.3390/su17083560 · Cited by 96
A comprehensive review of Digital Twin technologies in smart cities
Huzzat, A., Anpalagan, A., Khwaja, A. S., Woungang, I., Alnoman, A. A., & Pillai, A. S. (2025) · Digital Engineering · doi:10.1016/j.dte.2025.100040 · Cited by 65
Metadata
- Authors
- Agostinelli, S., Cumo, F., Guidi, G., & Tomazzoli, C.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2021-04-20
- Volume
- 14
- Issue
- 8
- Pages
- 2338
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W3156321386
- Cited by
- 280
- References
- 33
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence. https://app.cochat.ai/papers/10.3390/en14082338
- The paper
- Agostinelli, S., Cumo, F., Guidi, G., & Tomazzoli, C. (2021). Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence. Energies. https://doi.org/10.3390/en14082338
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
