Parallel to the need for new technologies and renewable energy resources to address sustainability, the emerging field of Artificial Intelligence (AI) has experienced con- tinuous high-speed growth in the application of its capabilities of modelling, manag- ing, processing, and making sense of data in the entire areas related to the production and management of energy. Moreover, the current trend indicates that the energy supply and management process will eventually be controlled by autonomous smart systems that optimize energy distribution operations based on integrative data-driven Machine Learning (ML) techniques or other types of computational methods.

Computations for Sustainability / Salavatidezfouli, S.; Nikishova, A.; Torlo, D.; Teruzzi, M.; Rozza, G.. - (2024), pp. 91-112. [10.1007/978-3-031-39311-2_7].

Computations for Sustainability

Torlo D.;Rozza G.
2024

Abstract

Parallel to the need for new technologies and renewable energy resources to address sustainability, the emerging field of Artificial Intelligence (AI) has experienced con- tinuous high-speed growth in the application of its capabilities of modelling, manag- ing, processing, and making sense of data in the entire areas related to the production and management of energy. Moreover, the current trend indicates that the energy supply and management process will eventually be controlled by autonomous smart systems that optimize energy distribution operations based on integrative data-driven Machine Learning (ML) techniques or other types of computational methods.
2024
Quantitative Sustainability
978-3-031-39310-5
reduced order methods; sustainability; machine learning; digital twins
02 Pubblicazione su volume::02a Capitolo o Articolo
Computations for Sustainability / Salavatidezfouli, S.; Nikishova, A.; Torlo, D.; Teruzzi, M.; Rozza, G.. - (2024), pp. 91-112. [10.1007/978-3-031-39311-2_7].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1707504
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