Electricity technological mix forecasting for life cycle assessment aware scheduling

Electricity technological mix forecasting for life cycle assessment aware scheduling

10.1016/j.procir.2020.01.099

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Bibliographic Details
Main Authors: Cornago, S., Vitali, A., Brondi, C., Low, J.S.C.
Other Authors: MECHANICAL ENGINEERING
Format: Conference or Workshop Item
Published: Elsevier B.V. 2021
Subjects:
Deep learning
Energy efficiency
Energy management
Machine learning
PEF
Product Environmental Footprint
Scheduling
Online Access:https://scholarbank.nus.edu.sg/handle/10635/197370
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Institution: National University of Singapore

Internet

https://scholarbank.nus.edu.sg/handle/10635/197370

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