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The electricity forecasting problem is among the prominent issues for policymakers to ensure a reliable and stable electricity supply. Although many studies have been executing effectively to predict China's electricity consumption and production, the results of diverse models are confusing and contradicting because they can hardly identify seasonal fluctuations and provide robust forecasts based on different sample volumes. This work focuses on using a new electricity forecasting tool to present precise results to overcome such shortcomings. Initially, a seasonal discrete grey model is designed based on the characteristics of China's electricity consumption and production. Besides, the rolling mechanism is introduced to improve forecasting accuracy further when performing the verification experiments. Secondly, the  DM ,  SPA , and  MCS  tests and level accuracy is implemented to measure each competing model's forecasting performance efficiently. Lastly, this new model's robustness over different sample sizes is validated by conducting numerous experiments with diverse sample volumes. Empirical results demonstrate that the technique is convincingly an accurate, robust, and applicable method for China's electricity consumption and production forecasting, outperforming any other prevalent forecasting model.

Electricity consumption and production forecasting considering seasonal patterns: An investigation based on a novel seasonal discrete grey model