Energy scenarios project future possibilities based on a variety of assumptions, yet do not fully account for inherent friction in the energy transition, particularly over the near term. A new study shows how machine learning can complement existing scenario tools by incorporating lessons from the past into projections for the future.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Craig, P. P., Gadgil, A. & Koomey, J. G. Annu. Rev. Environ. Resour. 27, 83–118 (2002).
Africa Energy Outlook 2019 (International Energy Agency, 2019).
Africa Power Sector: Planning and Prospects for Renewable Energy (International Renewable Energy Agency 2015).
Lucas, P. L. et al. Energy Policy 86, 705–717 (2015).
Alova, G., Trotter, P. A. & Money, A. Nat. Energy https://doi.org/10.1038/s41560-020-00755-9 (2020).
Krey, V. Wiley Interdiscip. Rev. Energy Environ. 3, 363–383 (2014).
Ou, S. et al. Nat. Energy 5, 666–673 (2020).
de Coninck, H. et al. in IPCC Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) Ch. 4 (WMO, 2018).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
McCollum, D.L. Machine learning for energy projections. Nat Energy 6, 121–122 (2021). https://doi.org/10.1038/s41560-021-00779-9
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41560-021-00779-9
This article is cited by
-
Coal Wettability Prediction Model Based on Small-Sample Machine Learning
Natural Resources Research (2024)
