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predictive cloud computing schedulingAccess research journals, articles, books and more | SpringerLink
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78% Informative
Eco4cast aims to reduce carbon footprint of machine learning models via predictive cloud computing scheduling.
Package uses advanced temporal convolution neural network to forecast daily carbon dioxide emissions stemming from electricity generation.
The code and documentation of the package are hosted on GitHub under the Apache 2.0 license.
Machine-learning models predict electricity price forecasts on the day-ahead market using machine learning.
Machine-learners predict solar power generation for large-scale photovoltaic plants in China and India using machine-learned models.
Machine learning-based time series models for effective CO2 emission prediction in India , Environ . Sci. Res. Res.
M. R. Qader , S. Khan , M. Kamal , and M. Haseeb , “Forecasting energy-related CO2 emissions employing a novel SSA-LSSVM model: Considering structural factors in China ,” Energies 11 , 781 ( 2018 ) “The carbon footprint of machine learning training will plateau, then shrink”.
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