Digital twin of a city: Review of technology serving city needs

Lehtola, Ville V.; Koeva, Mila; Elberink, Sander Oude; Raposo, Paulo; Virtanen, Juho-Pekka; Vahdatikhaki, Faridaddin; Borsci, Simone

doi:10.1016/j.jag.2022.102915

Cited by 69 publications

(20 citation statements)

References 122 publications

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“…One of the advantages of digital twinning is to save money and time by improving the planning and scheduling of activities through twinning modeling. At present, a small number of researches have begun to apply artificial intelligence to the research of building smart city and digital twin [79,81], but the application of ensemble learning in a single field cannot meet people's pursuit of intelligent life. Further research could consider how to use ensemble learning with other modules of artificial intelligence to develop an intelligent system to build a small part of a digital smart city.…”

Section: The Gap and Further Studymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

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Section: The Gap and Further Studymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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“…Moreover, different DT systems could be present based on the application areas of the systems or processes (e.g., component/parts twins, asset twins, systems/unit twins, and process twins) [35]. Furthermore, these systems could co-exist within them due to the advantages sought after by many industries, including the power-generation/energy sector [38,43,44], structural engineering [45,46], manufacturing [47,48], logistics and operations [45,49], healthcare [50,51], automotive and aerospace [52,53], urban planning [54,55], and other prospective applications [55][56][57][58]. With the progressive potential of technological innovations through DT, markets predict this technology will grow from USD 6.9 billion to USD 73.5 billion from 2022 to 2027 [59].…”

Section: Digital Twin (Dt) Technologymentioning

confidence: 99%

Developing a Skilled Workforce for Future Industry Demand: The Potential of Digital Twin-Based Teaching and Learning Practices in Engineering Education

Hazrat,

Hassan,

Chowdhury

et al. 2023

Sustainability

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Engineering education providers should foresee the potential of digital transformation of teaching and skill-developing activities so that graduating engineers can find themselves highly aligned with the demands and attributes needed by prospective industrial employers. The advancement of industrial revolutions towards hybridisation of the enabling technologies recognised by Industry 4.0, Society 5.0, and Industry 5.0 have transformed the components of the engineering higher education system remarkably. Future workforce requirements will demand an employee’s multidisciplinary skill mix and other professional qualities. Implementing human-centric decision-making based on insights from the Digital Twin (DT) systems, sustainability, and lean systems is necessary for further economic growth. Recent barriers identified by the Australian Council of Engineering Deans, the development of teaching capabilities, and affordable and digitally transformed learning facilities by education providers were all considered. This paper explores the role of Digital Twins (DTs) in enhancing engineering higher education by incorporating Industry 4.0 components and other industrial advances. By reviewing curricula, pedagogy, and the evolving skill requirements for engineering graduates, this study identifies key benefits of DTs, such as cost-effectiveness, resource management, and immersive learning experiences. This paper also outlines challenges in implementing DT-based labs, including IT infrastructure, data quality, privacy, and security issues. The findings indicate that engineering education should embrace DTs to foster multidisciplinary skills and human-centric decision-making to meet future workforce demands. Collaboration with industry is highlighted as a crucial factor in the successful transformation of teaching practices and in offering real-world experiences. The COVID-19 pandemic has expedited the adoption of DT technologies, demonstrating their utility in minimising educational disruptions. While this paper acknowledges the high potential of DTs to prepare engineering students for future industry demands, it also emphasises the need for professional development among educators to ensure effective and balanced implementation.

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“…Open datasets from urban areas are changing how these places are planned, simulated, and visualized. In particular, digital twin platforms enable informed decisions and avoid costly ad-hoc problem-solving by facilitating the inclusion of stakeholders, because everyone can be updated to have the same and the latest information [1]. Such platforms have been used for various applications, including energy forecasting, emergency planning, operational optimization, participatory planning, policy development, and scenario modeling [2].…”

Section: Introductionmentioning

confidence: 99%

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

Mosteiro-Romero,

Miller,

Quintana

et al. 2023

J. Phys.: Conf. Ser.

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The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university’s Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the access point to which it is connected. The Wi-Fi dataset contains 120 different buildings on campus and 10,300 anonymized individual devices. Activities are then assigned to each location on campus according to the building use type. In order to test the methodology, the activity plans of 27,604 undergraduate students, 8,304 graduate students, and 12,018 employees were simulated over a workweek. The results show the model’s ability to produce plausible activity plans but could be improved by implementing sampling rules and expanding the source dataset to include off-peak dates. Nevertheless, using such an agent-based modeling approach at the district scale appears to be a promising methodology to assess the impacts of different planning strategies on occupant behavior and district energy demand.

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Digital twin of a city: Review of technology serving city needs

Cited by 69 publications

References 122 publications

Ensemble Learning Applications in Multiple Industries: A Review

Ensemble Learning Applications in Multiple Industries: A Review

Developing a Skilled Workforce for Future Industry Demand: The Potential of Digital Twin-Based Teaching and Learning Practices in Engineering Education

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

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