The 8th International Conference on Technology and Energy Management

فارسی

09215069281 Ictem@ustmb.ac.ir, ictem2023@gmail.com

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Full Professor at Castilla-La Mancha University, Spain

Email: FaustoPedro.Garcia@uclm.es

Fausto works at UCLM as Full Professor (Accredited as Full Professor from 2013), Spain, Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute, and he has been Senior Manager in Accenture (2013-2014). He obtained his European PhD with a maximum distinction. He has been distingueed with the prices: Advancement Prize for Management Science and Engineering Management Nominated Prize (2018), First International Business Ideas Competition 2017 Award (2017); Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM); Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65 % ISI, 30% JCR and 92% internationals), some recognized as: “Renewable Energy” (as “Best Paper 2014”); “ICMSEM” (as “excellent”), “Int. J. of Automation and Computing” and “IMechE Part F: J. of Rail and Rapid Transit” (most downloaded), etc. He is author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, Intech, IGI, Marcombo, AlfaOmega,…), and 5 patents. He is Editor of 5 Int. Journals, Committee Member more than 40 Int. Conferences. He has been Principal Investigator in 4 European Projects, 5 National Projects, and more than 150 projects for Universities, Companies, etc. His main interest are: Maintenance Management, Renewable Energy, Transport, Advanced Analytics, Data Science. He is Director of www.ingeniumgroup.eu

Specialties: Maintenance Management, Operation Research, Railway, Wind Energy, Life Cicle Cost, Fault Tree Analysis, Predictive Maintenance, Logistic.

Abstract:

Management by Artificial Intelligence and Review and New Challenges

To-date, most of the energy sector’s transition efforts have focused on hardware: new low-carbon infrastructure that will replace legacy carbon-intensive systems. Relatively little effort and investment has focused on another critical tool for the transition: next-generation digital technologies, in particular artificial intelligence (AI). These powerful technologies can be adopted more quickly at larger scales than new hardware solutions, and can become an essential enabler for the energy transition.

AI is already proving its value to the energy transition in multiple domains, driving measurable improvements in renewable energy forecasting, grid operations and optimization, coordination of distributed energy assets and demand-side management, and materials innovation and discovery. AI holds far greater potential to accelerate the global energy transition, but it will only be realized if there is greater AI innovation, adoption and collaboration across the industry.

The principles define the actions that are needed to unlock AI’s potential in the energy sector across three critical domains:

Governing the use of AI:
Standards – implement compatible software standards and interoperable interfaces.
Risk management – agree upon a common technology and education approach to managing the risks presented by AI.
Responsibility – ensure that AI ethics and responsible use are at the core of AI development and deployment.
Designing AI that’s fit for purpose:
Automation – design generation equipment and grid operations for automation and increased autonomy of AI.
Sustainability – adopt the most energy-efficient infrastructure as well as best practices around sustainable computing to limit the carbon footprint of AI.
Design – focus AI development on usability and interpretability.
Enabling the deployment of AI at scale:
Data – establish data standards, data-sharing mechanisms and platforms to increase the availability and quality of data.
Education – empower consumers and the energy workforce with a human-centred AI approach and invest in education to match technology and skill development.
Incentives – create market designs and regulatory frameworks that allow AI use cases to capture the value that they create.