基于TCN与轻量Autoformer的电力负荷预测OA

The accuracy of power load forecasting is crucial for energy conservation and emission reduction,and higher accuracy can enable power companies to make more reasonable plans and improve economic benefits.Although Autoformer,based on the improved Transformer architecture,has achieved good results in sequence prediction tasks,it did not fully consider the causal rela-tionship of time when extracting temporal features,and there is too much redundant information in the attention layer,which leads to a decrease in model accuracy and memory consumption.To address these issues,this paper proposes a power load forecasting method that combines Time Convolutional Network(TCN)and an improved lightweight Autoformer model.Firstly,a time convolu-tional network is introduced into the Autoformer encoder to provide a larger receptive field and fully consider the causal relation-ship of the samples.Then,a distillation mechanism is added between the autocorrelation attention layers to reduce the number of model parameters.Finally,the results of experiment on five public datasets showed that the lightweight Autoformer combined with TCN reduced MSE and MAE by 8.95%to 32.40%and 4.91%to 15.51%respectively compared to the original model,and the prediction performance is significantly better than the other four mainstream methods,demonstrating its excellent performance.