Computations for Sustainability
Published in Quantitative Sustainability. Springer, Cham., 2024
Recommended citation: Salavatidezfouli, S., Nikishova, A., Torlo, D., Teruzzi, M., Rozza, G. (2024). Computations for Sustainability. In: Fantoni, S., Casagli, N., Solidoro, C., Cobal, M. (eds) Quantitative Sustainability. Springer, Cham. https://doi.org/10.1007/978-3-031-39311-2_7 https://doi.org/10.1007/978-3-031-39311-2_7
Parallel to the need for new technologies and renewable energy resources to address sustainability, the emerging field of Artificial Intelligence (AI) has experienced continuous high-speed growth in the application of its capabilities of modelling, managing, 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.
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