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

• 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.