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Seasonal dynamics in the prediction of household-level energy poverty: A machine learning approach

Boram Moon & J. Hong

Energy Policy2026https://doi.org/10.1016/j.enpol.2026.115146article
AJG 2ABDC A
Weight
0.50

What the paper says

• Machine learning predicts seasonal energy poverty using 330,960 household-month data. • Korea's energy poverty is driven mainly by heating-related structural vulnerability. • Income risk groups face summer vulnerability mainly due to socioeconomic limits. • Income risk groups face winter vulnerability mainly due to structural limits. • Double risk groups face persistently high vulnerability across both seasons.

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https://doi.org/https://doi.org/10.1016/j.enpol.2026.115146

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@article{boram2026,
  title        = {{Seasonal dynamics in the prediction of household-level energy poverty: A machine learning approach}},
  author       = {Boram Moon & J. Hong},
  journal      = {Energy Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.enpol.2026.115146},
}

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Seasonal dynamics in the prediction of household-level energy poverty: A machine learning approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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