Power load forecasting based on improved Transformer
This paper proposes a Transformer model for the power load forecasting task.The fully connected layer is used to replace the original decoder structure,which reduces the complexity of the model and makes the model more suitable for the power load data.The AdamW optimization method is used to optimize the defects of weight decay processing that are common in deep learning.Experimental results show that,compared with ELM,RNN,LSTM and traditional Transformer models,the improved Transformer model can more accurately predict power loads on the real power load datasets of Los Angeles,New York and Sacramento.
NETL
NSTL
万方数据
维普
