Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

Biagioni, David; Farrell, John T.; Garikapati, Venu; Gräf, Peter; Guba, Nalinrat; Hou, Yi; Jones, Wesley; Severino, Joe; Sigler, Devon; Todd, Austin; Ugirumurera, Juliette; Wang, Qichao; Young, Stan

doi:10.2172/1812196

2021

DOI: 10.2172/1812196

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Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

David Biagioni¹,

John T. Farrell²,

Venu Garikapati³

et al.

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Cited by 3 publications

References 7 publications

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Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Shen¹,

Arraño-Vargas²,

Konstantinou³

2022

digitaltwin

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Artificial Intelligence (AI) promises solutions to the challenges raised by the digitalization of power grids and their assets. Decision-making, forecasting and even operational optimization of grids and assets are just some of the solutions that AI algorithms can provide to operators, utilities and vendors. Nevertheless, barriers such as access to quality datasets, interpretability, repeatability, and availability of computational resources currently limit the extent of practical AI implementations. At the same time, Digital Twins (DTs) are foreseen as platforms that can overcome these barriers, and also provide a new environment for the development of enhanced and more intelligent applications. In this manuscript, we review the published literature to determine the existing capabilities and implementation challenges of AI algorithms in power systems, and classify AI-based applications based on their time scale to reveal their temporal sensitivity. By combining AI and DT, we outline multiple prospective use cases for AI-enhanced power grid and power asset DTs. Our review also identifies that the combination of AI-based solutions and DTs leverages new applications with the potential to fundamentally change multiple aspects of the power industry.

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Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Shen¹,

Arraño-Vargas²,

Konstantinou³

2022

digitaltwin

View full text Add to dashboard Cite

show abstract

Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Shen¹,

Arraño-Vargas²,

Konstantinou³

2023

digitaltwin

View full text Add to dashboard Cite

Artificial Intelligence (AI) promises solutions to the challenges raised by the digitalization of power grids and their assets. Decision-making, forecasting and even operational optimization of grids and assets are just some of the solutions that AI algorithms can provide to operators, utilities and vendors. Nevertheless, barriers such as access to quality datasets, interpretability, repeatability, and availability of computational resources currently limit the extent of practical AI implementations. At the same time, Digital Twins (DTs) are foreseen as platforms that can overcome these barriers, and also provide a new environment for the development of enhanced and more intelligent applications. In this manuscript, we review the published literature to determine the existing capabilities and implementation challenges of AI algorithms in power systems, and classify AI-based applications based on their time scale to reveal their temporal sensitivity. Furthermore, DT-based technologies are discussed, identifying the potentials to tackle current limitations of real-world AI applications as well as exploring the synergies between DTs and AI. By combining AI and DT, we outline multiple prospective use cases for AI-enhanced power grid and power asset DTs. Our review also identifies that the combination of AI-based solutions and DTs leverages new applications with the potential to fundamentally change multiple aspects of the power industry.

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Socio-Economic Impacts Resulting From The Integration Of Artificial Intelligence Into Electronic Surveillance Systems In Traffic

Samastı

2024

Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi

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In the last decade, electronic surveillance systems have been actively employed for monitoring traffic rule violations with the aim of enhancing traffic regulation. The utilization of these systems has resulted in increased compliance with traffic regulations, consequently leading to a reduction in losses attributed to traffic accidents. The impact created by these systems is expected to be further amplified through the incorporation of artificial intelligence (AI) support. Within the scope of this study, a detailed analysis of the socio-economic impact of AI-assisted Electronic Traffic Monitoring Systems has been conducted, focusing on economic, mobility, health, environmental, and quality of life aspects.

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Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

Cited by 3 publications

References 7 publications

Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities

Socio-Economic Impacts Resulting From The Integration Of Artificial Intelligence Into Electronic Surveillance Systems In Traffic

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