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Forecasting the residential electricity consumption can provide an effective and sustainable electricity supply in the rapid development of urbanization and industrialization. However, it is a challenging task to establish an efficient forecasting model for enhancing the forecasting accuracy, accelerating the forecasting speed, minimizing the electricity consumption and reducing the environmental pollution. Most existing works on the residential electricity consumption focus on improving the forecasting accuracy while the improvement is insufficient. And few of them have considered the forecasting speed. To overcome these limitations, this paper firstly proposes a hybrid Improved Whale Optimization Algorithm - Optimized Grey Seasonal Variation Index (IWOA-OGSVI) model for best solutions of satisfactory forecasting results with high accuracy and fast-convergence. Next, for evaluating the forecasting performance, two case studies are carried out to estimate the effectiveness of our proposed model compared with a range of benchmark models. Finally, the empirical results present that the MAPE values in our proposed model are smallest at the “Excellent” level of the forecasting power and all of them have converged before 30 iterations, which demonstrate the superiority of our proposed model over the others on the forecasting accuracy and speed among the five selected regions.