Abstract
The concept of Digital Twins offers the possibility of moving work from a physical environment to a virtual or digital environment and the ability to predict asset conditions in the future, or when it is physically undesirable, by exploiting the digital model. This in turn leads to significant reductions in the resources required to design, produce and maintain assets and resources. In the field of energy management, DTs are also starting to be considered as valuable analysis tools, as a digital twin facilitates real-time synchronisation between a real-world model (physical model) and its virtual copy for improved energy monitoring, prediction, and efficiency enhancement; thus, it can significantly reduce the overall energy consumption. A typical problem of DTs is the management of the data to be fed from the physical twin to the DT (and possibly the other way around), as one has to decide whether to store them within the DT or not, and one also has to decide whether to use different (depending on the data sources) or unified data governance models. To this end, an energy data space is proposed to allow the management of the necessary data in a way that is more functional to the DT concept.
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Rucco, C., Longo, A., Zappatore, M. (2022). Supporting Energy Digital Twins with Cloud Data Spaces: An Architectural Proposal. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_5
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