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Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power\u2010driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power\u2010driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power\u2010driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real\u2010world datasets are conducted, and the results indicate that the power\u2010driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. 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