Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

Authors

  • Praveen Tomar Office of Gas and Electricity Markets image/svg+xml
  • Veena Grover Noida Institute of Engineering and Technology

DOI:

https://doi.org/10.4108/ew.4825

Keywords:

Artificial Intelligence, Data Science, Climate Change, Energy Sector

Abstract

The energy sector, both in the UK and globally, faces significant challenges in the pursuit of sustainability and efficient resource utilization. Climate change, resource depletion, and the need for decarbonization demand innovative solutions. This analytical research paper examines the key challenges in the energy sector and explores how generative AI, digital twins, AI, and data science can play a transformative role in addressing these challenges. By leveraging advanced technologies and data-driven approaches, the energy sector can achieve greater efficiency, optimize operations, and facilitate informed decision-making. Artificial Intelligence (AI) involves replicating human-like intelligence in machines, enabling them to execute tasks that typically demand human cognitive capabilities like perception, reasoning, learning, and problem[1]solving. AI encompasses various methodologies and technologies, such as machine learning, natural language processing, computer vision, and robotics. Its adoption in the energy sector carries significant promise for addressing critical concerns and revolutionizing the industry. An overarching challenge in the energy sector revolves around enhancing energy efficiency, and AI emerges as a pivotal tool for optimizing energy utilization and curbing wastage. By analyzing vast amounts of data from various sources such as sensors, smart meters, and historical energy consumption patterns, AI algorithms can identify patterns and anomalies that humans may not detect. This enables the development of predictive models and algorithms that optimize energy consumption, leading to significant energy savings.

Downloads

Download data is not yet available.

References

Kwok, A.H., Doyle, E.E.H., Becker, J., Johnston, D., Paton, D.: What is ‘social resilience’? Perspectives of disaster researchers, emergency management practitioners, and policymakers in New Zealand. Int. J. Disaster Risk Reduct. 2016, Vol.19, Page 197–211 DOI: https://doi.org/10.1016/j.ijdrr.2016.08.013

Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C., Hu, Z.: A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies. Struct. Multidiscip. 2022 Vol-65, page-354 DOI: https://doi.org/10.1007/s00158-022-03425-4

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S.D., Tegmark, M., Fuso Nerini, F.: The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020 Vol 11, page 233 DOI: https://doi.org/10.1038/s41467-019-14108-y

Reddy Shabad, P.K., Alrashide, A., Mohammed, O.: Anomaly Detection in Smart Grids using Machine Learning. In: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. pp. 1–8(2021). DOI: https://doi.org/10.1109/IECON48115.2021.9589851

Meske, C., Osmundsen, K., Junglas, I.: Designing and Implementing Digital Twins in the Energy Grid Sector. MIS Q. Exec. 20, 183–198 (2021). DOI: https://doi.org/10.17705/2msqe.00048

International Energy Agency (IEA). (2021). Global Energy Review 2021. Retrievedfrom International conference, London 2021

Website: https://www.iea.org/reports/global-energy-review-2021.

Website: United Nations Development Programme (UNDP). (2021). Sustainable Development Goals.

Website: https://www.undp.org/sustainable-development-goals

Website: https://www.eon.com/en/about-us/media/press-release/2022/e.on-sets-all-course-for-digitalization.html

Downloads

Published

11-01-2024

How to Cite

1.
Tomar P, Grover V. Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis . EAI Endorsed Trans Energy Web [Internet]. 2024 Jan. 11 [cited 2024 Dec. 2];10. Available from: https://publications.eai.eu/index.php/ew/article/view/4825