Comparing machine learning and linear regression methods for estimating marginal greenhouse gas emission factors of electricity generation with renewables / prepared by Marie Pied, Sophie Pelland, Steven... : M154-151/2021E-PDF
"The electricity sector is key to reducing greenhouse gas emissions: energy supply across sectors is shifting towards electricity, and electricity production in turn is shifting towards low carbon sources. Marginal greenhouse gas emission factors (MEFs) are needed to properly quantify the impact on greenhouse gas emissions of policies and programs that modify electricity demand. We compared different methods of estimating MEFs applied to the Canadian provinces of Ontario and Alberta: a new method using artificial neural networks and existing methods using simple and multiple linear regression"--Executive summary, page i.
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publications.gc.ca/pub?id=9.917108&sl=1
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| Titre | Comparing machine learning and linear regression methods for estimating marginal greenhouse gas emission factors of electricity generation with renewables / prepared by Marie Pied, Sophie Pelland, Steven Wong, Dave Turcotte and Vahid Raissi Dehkordi. |
| Type de publication | Monographie |
| Langue | [Anglais] |
| Format | Texte numérique |
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| Description | 1 online resource (ix, 33 pages) : charts |
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