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.
Electricity Technological Mix Forecasting for Life Cycle Assessment Aware Scheduling
Andrea Vitali;Carlo Brondi;
2020
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.File in questo prodotto:
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