Long-term rolling prediction of transformer power load capacity based on the informer model

Abstract

Abstract There are currently no methods capable of long-term forecasting based on very long-term real-world data. Any false prediction may damage the electrical transformer. For this problem, a transformer power load based on the Informer model is used as a method of long-term rolling forecasting. This method uses the self-attention distillation mechanism in the Informer model to allow the decoder of each layer to shorten the length of the input sequence by half, thus greatly saving the encoder memory overhead, and this paper adds a rolling long-term prediction function, making the prediction decoding time extremely short. Taking the power load data of a certain province in China as a test case, the improved model Informer* was compared with the traditional Informer model. The results show that the prediction accuracy of the Informer* model is higher and the load prediction accuracy is effectively improved.