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Recent advances on industrial data-driven energy savings: Digital twins and infrastructures
Renewable and Sustainable Energy Reviews ( IF 16.3 ) Pub Date : 2020-08-21 , DOI: 10.1016/j.rser.2020.110208
Sin Yong Teng , Michal Touš , Wei Dong Leong , Bing Shen How , Hon Loong Lam , Vítězslav Máša

Data-driven models for industrial energy savings heavily rely on sensor data, experimentation data and knowledge-based data. This work reveals that too much research attention was invested in making data-driven models, as supposed to ensuring the quality of industrial data. Furthermore, the true challenge within the Industry 4.0 is with data communication and infrastructure problems, not so significantly on developing modelling techniques. Current methods and data infrastructures for industrial energy savings were comprehensively reviewed to showcase the potential for a more accurate and effective digital twin-based infrastructure for the industry. With a few more development in enabling technologies such as 5G developments, Internet of Things (IoT) standardization, Artificial Intelligence (AI) and blockchain 3.0 utilization, it is but a matter of time that the industry will transition towards the digital twin-based approach. Global government efforts and policies are already inclining towards leveraging better industrial energy efficiencies and energy savings. This provides a promising future for the development of a digital twin-based energy-saving system in the industry. Foreseeing some potential challenges, this paper also discusses the importance of symbiosis between researchers and industrialists to transition from traditional industry towards a digital twin-based energy-saving industry. The novelty of this work is the current context of industrial energy savings was extended towards cutting-edge technologies for Industry 4.0. Furthermore, this work proposes to standardize and modularize industrial data infrastructure for smart energy savings. This work also serves as a concise guideline for researchers and industrialists who are looking to implement advanced energy-saving systems.



中文翻译:

工业数据驱动节能的最新进展:数字双胞胎和基础设施

数据驱动的工业节能模型在很大程度上依赖于传感器数据,实验数据和基于知识的数据。这项工作表明,为了确保工业数据的质量,在建立数据驱动的模型上投入了过多的研究注意力。此外,Industry 4.0的真正挑战在于数据通信和基础架构问题,而不是开发建模技术方面的重大挑战。对用于工业节能的当前方法和数据基础设施进行了全面审查,以展示为该行业提供更准确和有效的基于数字孪生的基础设施的潜力。随着5G开发,物联网(IoT)标准化,人工智能(AI)和区块链3.0利用等支持技术方面的进一步发展,业界将过渡到基于数字孪生的方法只是时间问题。全球政府的努力和政策已经倾向于利用更好的工业能源效率和节能。这为行业中基于数字双胞胎的节能系统的发展提供了广阔的前景。预见了一些潜在的挑战,本文还讨论了研究人员和工业家之间共生对于从传统工业向数字双核节能产业过渡的重要性。这项工作的新颖之处在于当前工业节能的背景已扩展到工业4.0的尖端技术。此外,这项工作还建议对工业数据基础架构进行标准化和模块化,以实现智能节能。

更新日期:2020-08-21
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