Recent advances on industrial data-driven energy savings: Digital twins and infrastructures

journal article in Web of Science

en

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.

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.

Digital twins, Data-driven energy savings, Artificial intelligence (AI), Blockchain, Internet of things (IoT), Cyber-physical production systems (CPPS)

21.01.2021

Elsevier

Oxford, England

1364-0321

135

1

1–22

22