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Machine learning for energy projections

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  • David L. McCollum

    (Electric Power Research Institute (EPRI))

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.

Suggested Citation

  • David L. McCollum, 2021. "Machine learning for energy projections," Nature Energy, Nature, vol. 6(2), pages 121-122, February.
    • Handle: RePEc:nat:natene:v:6:y:2021:i:2:d:10.1038_s41560-021-00779-9
    DOI: 10.1038/s41560-021-00779-9
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    Cited by:

    1. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Trotter, Philipp A., 2022. "The slow transition to solar, wind and other non-hydro renewables in Africa – Responding to and building on a critique by Kincer, Moss and Thurber (2021)," World Development Perspectives, Elsevier, vol. 25(C).
    3. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.

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