The traditional gray prediction theory requires less sample size and simple modeling, but it ignores new information that is more beneficial to prediction, and it is easy to cause the phenomenon of aging of the prediction model, and the prediction accuracy is not high. Therefore, a more scientific and reasonable GM (1,1) gray prediction model based on metabolism is proposed for the problem of urban electricity forecasting. This method avoids the limitations of local information modeling, and the metabolic processing of removing the oldest data of the original sequence of each prediction result ensures the effectiveness of the prediction sequence. The application of model prediction with Jurong city electricity data and the programming calculation of improved GM(1,1) model with Mtalab verify the practicability.
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