应用数字孪生概念促进建筑、校园、社区和城市规模的热传导

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data and Cognitive Computing Pub Date : 2023-08-25 DOI:10.3390/bdcc7030145
Ekaterina Lesnyak, Tabea Belkot, Johannes Hurka, Jan Philipp Hörding, L. Kuhlmann, Pavel Paulau, Marvin Schnabel, P. Schönfeldt, Jan Middelberg
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引用次数: 0

摘要

热转型是能源转型的核心支柱,旨在脱碳并提高私营和工业部门供热的能源效率。一方面,这是通过用可再生能源取代化石燃料来实现的。另一方面,它涉及到减少总热量消耗以及相关的传输和通风损失。除了翻新,数字化也起到了重要作用。尽管对用于不同尺度热转换的数字孪晶(DT)进行了大量研究,但热优化的跨尺度视角仍有待发展。为了应对这一研究空白,本研究考察了四个不同规模的应用DT实例:建筑、校园、社区和城市。该研究比较了它们的目标和概念框架,同时也确定了共同的挑战和潜在的协同作用。研究结果表明,所有DT量表都面临着类似的数据相关挑战,如收集、所有权、连接性和可靠性。此外,DTs之间确定了层次协同,这意味着需要合作和交流。为此,“Wärmewende”数据平台促进了与内部和外部利益相关者的研究数据和知识交流。
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Applied Digital Twin Concepts Contributing to Heat Transition in Building, Campus, Neighborhood, and Urban Scale
The heat transition is a central pillar of the energy transition, aiming to decarbonize and improve the energy efficiency of the heat supply in both the private and industrial sectors. On the one hand, this is achieved by substituting fossil fuels with renewable energy. On the other hand, it involves reducing overall heat consumption and associated transmission and ventilation losses. In addition to refurbishment, digitalization contributes significantly. Despite substantial research on Digital Twins (DTs) for heat transition at different scales, a cross-scale perspective on heat optimization still needs to be developed. In response to this research gap, the present study examines four instances of applied DTs across various scales: building, campus, neighborhood, and urban. The study compares their objectives and conceptual frameworks while also identifying common challenges and potential synergies. The study’s findings indicate that all DT scales face similar data-related challenges, such as gathering, ownership, connectivity, and reliability. Also, hierarchical synergy is identified among the DTs, implying the need for collaboration and exchange. In response to this, the “Wärmewende” data platform, whose objectives and concepts are presented in the paper, promotes research data and knowledge exchange with internal and external stakeholders.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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