Research on Electric Vehicle Charging Load Forecasting Method Based on Improved LSTM Neural Network

Abstract

Targeting the problem whereby electric vehicle charging loads have large temporal randomness, which affects the accuracy of load prediction, an electric vehicle charging load prediction method based on an improved long short-term memory (LSTM) neural network is investigated. The similarity of EV charging load curves is calculated and the data related to EV charging loads are clustered according to the similarity using a spectral clustering algorithm. The principal component analysis method is used to extract the principal components from the clustering results of EV load data. The LSTM neural network takes the main components of EV charging load as inputs, updates the state of the storage unit through the activation function, introduces an attention mechanism to improve the structure of the network, and outputs the prediction results of the EV charging load through the operation of the input gate, forgetting gate, and output gate. The experimental results show that this method can accurately predict the hourly and daily charging loads of electric vehicles and provide support for their orderly charging of electric vehicles.