TY - GEN
T1 - Digital Twins in Manufacturing
T2 - 2025 IEEE PES/IAS PowerAfrica Conference: Pioneering Sustainable Energy Solutions for Africa's Future, PAC 2025
AU - Dooley, Adam
AU - O'brien, William
AU - Penica, Mihai
AU - Boyle, Fiona
AU - Mcgrath, Sean
AU - O'connell, Eoin
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Digital twins (DTs) are transforming the manufacturing sector by enabling real-time monitoring, predictive maintenance, and process optimization. This paper systematically reviews the ten most cited research papers on digital twins in manufacturing, identifying key themes, successes, challenges, and future research directions. While rooted in the manufacturing domain, the findings reveal insights highly transferable to renewable energy and power systems - particularly in areas such as data-driven decision-making, AIdriven predictive modelling, and cyber-physical system integration. Notably, the technologies and methodologies explored - such as real-time data synchronization, simulation-based planning, and AI-enhanced analytics - can support smart grid optimization, renewable energy forecasting, and infrastructure resilience. The paper advocates for greater crosssector adoption of digital twin frameworks, particularly in energy system planning, energy efficiency strategies, and sustainability-driven innovation. This positions digital twins as not only pivotal for Industry 4.0 but also as enablers of the global transition toward secure, efficient, and intelligent energy ecosystems.
AB - Digital twins (DTs) are transforming the manufacturing sector by enabling real-time monitoring, predictive maintenance, and process optimization. This paper systematically reviews the ten most cited research papers on digital twins in manufacturing, identifying key themes, successes, challenges, and future research directions. While rooted in the manufacturing domain, the findings reveal insights highly transferable to renewable energy and power systems - particularly in areas such as data-driven decision-making, AIdriven predictive modelling, and cyber-physical system integration. Notably, the technologies and methodologies explored - such as real-time data synchronization, simulation-based planning, and AI-enhanced analytics - can support smart grid optimization, renewable energy forecasting, and infrastructure resilience. The paper advocates for greater crosssector adoption of digital twin frameworks, particularly in energy system planning, energy efficiency strategies, and sustainability-driven innovation. This positions digital twins as not only pivotal for Industry 4.0 but also as enablers of the global transition toward secure, efficient, and intelligent energy ecosystems.
KW - Digital Twins
KW - Industry 4.0
KW - Predictive Maintenance
KW - Renewable Energy
KW - Smart Grids.a
UR - https://www.scopus.com/pages/publications/105031878117
U2 - 10.1109/PowerAfrica65840.2025.11289141
DO - 10.1109/PowerAfrica65840.2025.11289141
M3 - Conference contribution
AN - SCOPUS:105031878117
T3 - Proceedings of the 2025 IEEE PES/IAS PowerAfrica Conference: Pioneering Sustainable Energy Solutions for Africa's Future, PAC 2025
BT - Proceedings of the 2025 IEEE PES/IAS PowerAfrica Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 September 2025 through 2 October 2025
ER -