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https://doi.org/10.1016/j.procir.2020.01.099
Title: | Electricity technological mix forecasting for life cycle assessment aware scheduling | Authors: | Cornago, S. Vitali, A. Brondi, C. Low, J.S.C. |
Keywords: | Deep learning Energy efficiency Energy management Machine learning PEF Product Environmental Footprint Scheduling |
Issue Date: | 2020 | Publisher: | Elsevier B.V. | Citation: | Cornago, S., Vitali, A., Brondi, C., Low, J.S.C. (2020). Electricity technological mix forecasting for life cycle assessment aware scheduling. Procedia CIRP 90 : 268-273. ScholarBank@NUS Repository. https://doi.org/10.1016/j.procir.2020.01.099 | Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International | Abstract: | Here we show the possibility to forecast the hourly day-ahead electricity consumption mix exploiting a deep learning model. Thus, in the context of the proposed life cycle assessment (LCA) aware scheduling framework, a production scheduling could be optimized to adapt its load profile in those hours that are predicted to have a lower environmental impact. The objective functions of the optimization would therefore be the LCA impacts of the consumed electricity mix. The increase in detail in the accounting can also be exploited to complement the life cycle inventory, allowing the overall assessment to be more adherent to reality. © 2020 The Author(s). Published by Elsevier B.V. | Source Title: | Procedia CIRP | URI: | https://scholarbank.nus.edu.sg/handle/10635/197370 | ISSN: | 22128271 | DOI: | 10.1016/j.procir.2020.01.099 | Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International |
Appears in Collections: | Students Publications |
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