Machine learning for energy projections

David L. McCollum ()
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David L. McCollum: Electric Power Research Institute (EPRI)

Nature Energy, 2021, vol. 6, issue 2, 121-122

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41560-021-00779-9

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