Publication detail

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

TENG, S.Y., TOUŠ, M., LEONG, W.D., HOW, B.S., LAM, H.L., MÁŠA, V.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

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

Authors

TENG, S.Y., TOUŠ, M., LEONG, W.D., HOW, B.S., LAM, H.L., MÁŠA, V.

Released

21. 1. 2021

Publisher

Elsevier

Location

Oxford, England

ISBN

1364-0321

Periodical

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Year of study

135

Number

1

State

United States of America

Pages from

1

Pages to

22

Pages count

22

URL

https://www.sciencedirect.com/science/article/pii/S1364032120304974

BibTex

@article{BUT170182,
  author="Sin Yong {Teng} and Michal {Touš} and Vítězslav {Máša}",
  title="Recent advances on industrial data-driven energy savings: Digital twins and infrastructures",
  journal="RENEWABLE & SUSTAINABLE ENERGY REVIEWS",
  year="2021",
  volume="135",
  number="1",
  pages="1--22",
  doi="10.1016/j.rser.2020.110208",
  issn="1364-0321",
  url="https://www.sciencedirect.com/science/article/pii/S1364032120304974"
}