Abstract
The introduction of building information modeling (BIM) in the design and monitoring of public buildings has seen increasing use over time. Likewise, there has been a growing interest in the IoT paradigm within the construction industry. This study aims to integrate the two approaches to optimize the electricity consumption of a public building through the interpretation of data collected by dedicated sensors. The article proposes the study of a process applied to a real case study and discusses and comments on the preliminary results obtained. Finally, a meeting point is found between the IoT technology and the BIM methodology through the definition of digital twin (DT), understood as a digital copy of practical reality both in the design phase and in monitoring and forecasting. The type of data collected by IoT sensors commonly falls under the big data paradigm, which is generally not analyzable through traditional techniques. Therefore, in order to extract new knowledge from historical data, deep learning techniques have been used, which are able to analyze and identify relationships between data in an intuitive way that otherwise could not be detected.
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References
Guida CG, Gupta BB, Lorusso A, Marongiu F, Santaniello D, Troiano A (2021) An integrated BIM-IoT approach to support energy monitoring. In: International conference on smart systems and advanced computing (Syscom-2021), CEUR workshop proceedings (CEUR-WS.org)
Lorusso A, Guida D (2022) IoT system for structural monitoring. In: International conference “new technologies, development and applications”. NT 2022: New technologies, development and application V. Springer, Cham, pp 599–606. https://doi.org/10.1007/978-3-031-05230-9_72
Dembele SP, Bellatreche L, Ordonez C, Roukh A (2020) Think big, start small: a good initiative to design green query optimizers. Cluster Comput 23(3):2323–2345. https://doi.org/10.1007/s10586-019-03005-0
Dembele SP, Bellatreche L, Ordonez C (2020) Towards green query processing—auditing power before deploying. In: 2020 IEEE international conference on big data (Big Data). IEEE, pp 2492–2501. https://doi.org/10.1109/BigData50022.2020.9377819
Quatrano A, De Simone MC, Rivera ZB, Guida D (2017) Development and implementation of a control system for a retrofitted CNC machine by using Arduino. FME Trans 45(4):565–571. https://doi.org/10.5937/fmet1704565Q
Metallo A (2022) Emissivity prediction for an IR camera during laser welding of aluminum. Int J Thermodyn 25(4):24–34. https://doi.org/10.5541/ijot.1129559
Melesse TY, di Pasquale V, Riemma S (2021) Digital twin models in industrial operations: state-of-the-art and future research directions. IET Collaborative Intell Manuf 3(1):37–47. https://doi.org/10.1049/cim2.12010
D’Aranno PJV, Di Benedetto A, Fiani M, Marsella M, Moriero I, Baena JAP (2021) An application of persistent scatterer interferometry (PSI) technique for infrastructure monitoring. Remote Sens 13(1052):1–23. https://doi.org/10.3390/rs13061052
Tsirogiannis D, Harizopoulos S, Shah MA (2010) Analyzing the energy efficiency of a database server. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, pp 231–242. https://doi.org/10.1145/1807167.1807194
Melesse TY, Bollo M, di Pasquale V, Riemma S (2022) Digital twin for inventory planning of fresh produce. IFAC-PapersOnLine 55(10):2743–2748. https://doi.org/10.1016/j.ifacol.2022.10.134
Matos R, Rodrigues F, Rodrigues H, Costa A (2021) Building condition assessment supported by Building Information Modelling. J Build Eng 38:102186. https://doi.org/10.1016/j.jobe.2021.102186
De Simone MC, Lorusso A, Santaniello D (2022) Predictive maintenance and structural health monitoring via IoT system. In: 2022 IEEE workshop on complexity in engineering (COMPENG). IEEE, pp 1–4. https://doi.org/10.1109/COMPENG50184.2022.9905441
Saeidi M, Cambron P, Kaymanesh A, Chandra A (2020) Wind farm generators thermal condition monitoring based on long short-term memory. In: 2020 IEEE international conference on power electronics, drives and energy systems (PEDES). IEEE, pp 1–6. https://doi.org/10.1109/PEDES49360.2020.9379349
Tomasiello S, Loia V, Khaliq A (2021) A granular recurrent neural network for multiple time series prediction. Neural Comput Appl 33(1):1–18. https://doi.org/10.1007/s00521-021-05791-4
Schizas N, Karras A, Karras C, Sioutas S (2022) TinyML for ultra-low power AI and large scale IoT deployments: a systematic review. Future Internet 14(12):363. https://doi.org/10.3390/fi14120363
Lai G, Chang W-C, Yang Y, Liu H (2018) Modeling long–and short–term temporal patterns with deep neural networks. In: SIGIR’18: the 41st international ACM SIGIR conference on research & development in information retrieval, pp 95–104. https://doi.org/10.1145/3209978.3210006
Casillo M et al (2021) A situation awareness approach for smart home management. In: 2021 International seminar on machine learning, optimization, and data science (ISMODE). IEEE, pp 260–265. https://doi.org/10.1109/ISMODE53584.2022.9742901
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Cecere, L., Colace, F., Lorusso, A., Marongiu, F., Pellegrino, M., Santaniello, D. (2024). IoT and Deep Learning for Smart Energy Management. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-99-3043-2_86
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