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Insights into advanced models for energy poverty forecasting

Author

Listed:
  • Montserrat González Garibay

    (Institute for Economic Research)

  • Kaja Primc

    (Institute for Economic Research)

  • Renata Slabe-Erker

    (Institute for Economic Research)

Abstract

The growing importance of long-term planning in European Union member states’ energy poverty policies makes it necessary to develop forecasting techniques to support related policy decision-making. The combination of machine learning and econometrics holds promise in the field provided that several crucial challenges are tackled.

Suggested Citation

  • Montserrat González Garibay & Kaja Primc & Renata Slabe-Erker, 2023. "Insights into advanced models for energy poverty forecasting," Nature Energy, Nature, vol. 8(9), pages 903-905, September.
    • Handle: RePEc:nat:natene:v:8:y:2023:i:9:d:10.1038_s41560-023-01311-x
    DOI: 10.1038/s41560-023-01311-x
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    Cited by:

    1. Al Kez, Dlzar & Foley, Aoife & Abdul, Zrar Khald & Del Rio, Dylan Furszyfer, 2024. "Energy poverty prediction in the United Kingdom: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).

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