Transfer learning based CNN-GRU short-term power load forecasting method
With the advent of the information era,data-driven machine learning has become a mainstream short-term power load forecasting method,but such method is highly dependent on data.In order to improve this deficiency,this paper proposes a hybrid prediction model based on convolutional neural network(CNN)and gated recurrent unit(GRU)of transfer learning.Two power load data sets A and B in northeast China are taken as examples to forecast and analyze the short-term power load,the results show that the proposed method can effectively improve the accuracy and reliability of short-term power load forecasting in the case of data scarcity.
power load forecastingconvolutional neural networktransfer learninggated recurrent unit
NETL
NSTL
万方数据
