Abstract: In the short-term load current prediction,the traditional model can’t solve the problems of nonlinearity and time dependence of load current data simultaneously.To solve this problem,this paper proposed a short-term high vol-tage load current regression prediction(SHCP) method based on a long short-term memory(LSTM) recurrent neural network,namely SHCP-LSTM.The proposed method introduces the weight of self-circulation,which can make cells connected with each other circularly and dynamically change the cumulative time scale in the prediction,thus having a long short memory function.Meanwhile,the method uses the forgetting gate to control the input and output,so that the gate control unit has the sigmoid nonlinearity.Experiments show that the method is feasible and effective.Compared with linear logistic regression algorithm(LR) and machine learning algorithm artificial neural network(ANN) and back propagation neural network(BPNN) prediction,SHCP-LSTM has fast convergence speed and high accuracy.

Key words: LSTM, Regression prediction, SHCP-LSTM, Short-termload current prediction