| Title: |
An analysis of the implementation of artificial intelligence in the energy sector |
| Authors: |
Amedeo Amoresano; Christian Andresen; Ricardo Bessa (; Guillermo Borragan; Massimo Celino; Marta Chinnici; Volker Hoffmann; Kourosh Malek; Lorenzo Malerba; Rafael Mayo-Garcia; Nicola Paltrinieri; Spyridon Pantelis; Signe Riemer-Sorensen; Marco Selig; Aysen Sivrikaya |
| Contributors: |
Amedeo Amoresano (Università degli Studi di Napoli Federico Il), Christian Andresen (SINTEF), Ricardo Bessa (INESC TEC), Guillermo Borragan (VITO), Massimo Celino (ENEA), Marta Chinnici (ENEA), Volker Hoffmann (SINTEF), Kourosh Malek (FZJ), Lorenzo Malerba (CIEMAT, Coordinator of the EERA Joint Programme Nuclear Materials), Rafael Mayo-Garcia (CIEMAT, Coordinator of the EERA Joint Programme Digitalisation far Energy), Nicola Paltrinieri (NTNU), Spyridon Pantelis (EERA), Signe Riemer-Sorensen (SINTEF), Marco Selig (DBFZ), and Aysen Sivrikaya (Hacettepe University); Rafael Mayo-Garcia (CIEMAT, Coordinator of the EERA Joint Programme Digitalisation far Energy); Amoresano, Amedeo; Andresen, Christian; (, Ricardo Bessa; Borragan, Guillermo; Celino, Massimo; Chinnici, Marta; Hoffmann, Volker; Malek, Kourosh; Malerba, Lorenzo; Mayo-Garcia, Rafael; Paltrinieri, Nicola; Pantelis, Spyridon; Riemer-Sorensen, Signe; Selig, Marco; Sivrikaya, Aysen |
| Publisher Information: |
EERA; BEL; Bruxelles |
| Publication Year: |
2025 |
| Collection: |
IRIS Università degli Studi di Napoli Federico II |
| Subject Terms: |
Energy; Electrical grids,Al and energy efficiency |
| Description: |
he European Commission defines Artificial lntelligence (Al) as software systems created by people that, when faced with a complex objective, operate in the physical or digitai realm by perceiving their environment, acquiring and interpreting structured or unstructured data, reasoning on the knowledge, processing that intormation, and deciding on the best actions to achieve their goal. Al is a key component of the fourth industriai revolution, and as in other sectors, it is already being applied in the energy industry. But how can it help meet Europe's decarbonisation goals? lts raie will be crucial in achieving a more sustainable future as we are now in a period where energy systems are becoming increasingly complex as demand grows and decarbonisation eftorts intensify. Yet a clean, modern and decarbonised grid will be vital in the broader move to a net-zero emissions economy. Some steps beyond are data centre operators exploring alternative power options, like nuclear technologies, to power sites or storage technologies such as hydrogen. Also, companies' investments in emerging tech such as carbon removal, to remove C02 from the air and stare it safely. As in the previous cases, Al can also play a raie in overcoming barriers to integrating the necessary vast amounts of renewable energy into existing grids. Far example, the variability in renewable energy production often results in overproduction during peak times and underproduction during lulls, leading to wasteful energy consumption and grid instability. By analysing vast datasets, from weather patterns to energy consumption trends, Al can forecast energy production with remarkable accuracy. Moreover, Al helps to simplify processes aimed at improving energy efficiency and facilitating the transiti on to renewable energy. |
| Document Type: |
book part |
| Language: |
English, Middle (1100-1500) |
| Relation: |
info:eu-repo/semantics/altIdentifier/isbn/978-2-931174-07-4; ispartofbook:An analysis of the implementation of artificial intelligence in the energy sector; volume:Primo; firstpage:18; lastpage:20; numberofpages:43; alleditors:Rafael Mayo-Garcia (CIEMAT, Coordinator of the EERA Joint Programme Digitalisation far Energy); https://hdl.handle.net/11588/1001437 |
| Availability: |
https://hdl.handle.net/11588/1001437 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Dominio pubblico ; license uri:http://creativecommons.org/publicdomain/zero/1.0/ |
| Accession Number: |
edsbas.A857C666 |
| Database: |
BASE |