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Smart city article details
| Title | Collaborative Digital Twins: The Case Of The Energy Communities |
|---|---|
| ID_Doc | 14710 |
| Authors | Adu-Kankam K.O.; Camarinha-Matos L.M. |
| Year | 2023 |
| Published | SN Computer Science, 4, 5 |
| DOI | http://dx.doi.org/10.1007/s42979-023-02050-2 |
| Abstract | The process of urbanization is accelerating on a global scale, leading to the rapid expansion of cities and urban settlements. However, this fast-paced growth poses significant challenges in terms of sustainable development and reducing greenhouse gas emissions. As cities grow, they require more infrastructure, produce greater amounts of waste, and consume more energy. To address the energy-related challenges, renewable energy communities (RECs) have emerged as a potential solution, capturing the attention of policymakers. RECs offer a range of benefits that contribute to the sustainability of communities and cities. To further enhance their effectiveness, this work proposes the concept of “Collaborative Digital Twins” (CDT) within a collaborating ecosystem. A CDT represents a replica of a household unit within the REC environment, equipped with cognitive intelligence to make rational and autonomous decisions that promote collaborative behaviors. Thus, CDTs can be viewed as intelligent digital twins that adopt a collaborative approach to problem-solving and decision-making. To demonstrate the cognitive and collaborative capabilities of CDTs, a prototype of a “Cognitive Household Digital Twins” (CHDT) community is presented using a multimethod simulation technique. The prototype explores various collaborative scenarios, revealing the potential of CDTs as a viable decision support system for RECs and smart cities. This research highlights the positive impact that collaborative digital twins can have on scaling development while simultaneously addressing some of sustainability challenges associated with urbanization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. |
| Author Keywords | Cognitive digital twins; Collaborative networks; Digital twins; IoT; Renewable energy communities |
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