The emergence of novel technologies is significantly contributing to the study of new approaches to the development of energy Cyber-Physical systems. Technologies like IoT, Big Data, Machine Learning and AI are becoming so tightly coupled to be considered as a singular technological framework. Such a framework is at the basis of the Digital Twin technology, which allows to interact with a real-world asset by referring to its digital counterpart. Being potentially applied to any kind of physical asset, the Digital Twin approach can create a synergy for a comprehensive modeling, monitoring, and forecasting of the same asset. In the energy domain, Digital Twins can facilitate the decision making by providing interactive dashboards to the stakeholders within a metaverse instance. These dashboards can support what-if analyses, forecasts on energy demand and production, and remote control of the physical assets to optimize their performance. This paper proposes a framework for developing a Cyber-Physical solution in the energy domain. The framework consists of three building blocks called Physical Asset, Data Infrastructure, and Digital Twin. The Physical Asset and the Digital Twin blocks correspond to the typical counterparts of a Cyber-Physical system, whilst the Data Infrastructure is delegated to support their synchronization and/or interaction. To assess the proposed framework, a real testbed was developed as Cyber-Physical system to implement the digital twin of a smart photovoltaic station.

Digital twins are increasingly being implemented in smart cities, where they are designed to provide 3D digital representations for near-real time interactivity and status feedback of the twinned physical assets. The ability to provide accurate predictions in what-if scenarios plays a key role in driving the advances in resources optimization and risk mitigation. To this end, prediction models require large datasets to train, and such datasets are usually scarce at local level. In this paper, we propose a generative AI-based workflow to infer domestic photovoltaic energy production data, based on a process that uses Generative Adversarial Networks to generate an enriched meteorological dataset. The generated meteorological data captures essential statistical properties of real data that can be later used to infer realistic energy production data points. The resulting output dataset is validated against a real photovoltaic system, highlighting the ability to deliver high-fidelity time series out of scarce input datasets.

Data spaces are driving smart city innovation by promoting interoperability, standardization, and predictive analytics, enabling proactive urban management and fostering innovation by creating a collaborative data ecosystem. The European Community is strongly promoting data spaces design initiatives. However, designers face complex challenges in complying with legal data regulations due to the lack of mature, unified solutions.This study aims to fill a gap in data space design literature by gathering key factors and scouting compliant implementations. It introduces European initiatives and their joint ventures, and identifies overlaps and lack of coordination between their proposals. Using these results, we formulate suggested actions and organize them into seven areas. For each given area, the most suitable European data space initiatives in terms of technical specifications and implementations are finally proposed to DS developers. The recommendations are intended to assist DS developers in identifying and navigating European DS design initiatives before selecting the most suitable proposal.

Edge-powered dataspaces enhance time-sensitive applications, efficient bandwidth, security, and privacy, particularly for smart urban environment data spaces. In addition, designing optimized distributed edge-powered dataspaces that consider the shared data residing near the provider’s ease the management and control over the data. However, since distributed edge devices are heterogeneous, application deployment and management are typically challenging. Container virtualization is an important technology that addresses this problem, specifically on devices with limited resources. Scaling the number of nodes does not always guarantee adequate performance improvements. Therefore, it is crucial to study the effect of horizontal scalability on runtime, throughput, latency, and scaling the record and operation counts. In this study, the MongoDB database has been benchmarked using Yahoo! Cloud Serving Benchmark with a Docker Container of a single node and a Docker Swarm cluster of nine nodes scaled horizontally on Raspberry Pi. In addition, the study scales the record and operation counts to understand their effect in a similar environment.

The growing importance of data spaces, which promote the exchange of data across various domains, has generated considerable attention. The existing body of research on the adoption of data space and federated data sharing has predominantly focused on technological factors. However, there is an increasing acknowledgment of the need for a more holistic perspective. This study presents an exploratory investigation based on the Grounded Theory methodology-based survey, that incorporates interviews with professionals from different sectors. Through this research, the most significant data key factors that drive the data space design as support for an Urban Digital Twin Platform. The findings obtained from this research indicate 4 main characteristics and 11 subcategories that are obtained in compliance with the European reference specifications in data space design. These findings can be used for matching the needs perceived from professionals with the European reference specifications. Regarding the matching results, some final considerations are provided.

Digital Twins (DTs) are the novel paradigm for the development of Cyber-Physical systems. The state of art presents several use cases in different domains, and one of the most complex examples is represented by the Urban Digital Twin (UDT), which aims to virtualize urban assets (e.g., buildings, mobility infrastructures, energy grids, waste management facilities, etc.) and build advanced analysis and prediction services upon a city’s digital representation. UDTs represent a formidable example of system of systems, as they are structured into a hierarchy of interconnected DT instances that process and share a huge amount of data subject to different access and usage policies.Regarding data management in complex distributed scenarios, Data Spaces are an emerging paradigm that aims at building a secure and privacy-preserving infrastructure to pool, access, share, process and use data. As a matter of fact, existing DTs solutions (not only in the urban domain) do not present a clear software architecture characterization and, moreover, they pay little attention to the data management aspects.To bridge this gap, in this paper we present the Digital Twin Space, an architectural proposal that aims to instantiate Data Spaces into DTs according to the guidelines set by relevant international projects. We use the UDT as a running example and we validate the model in the case of a smartPV panel, showing that the model matches the real cyber-physical system.