Open AccessElectric Vehicle Load Prediction Method Based on Back-Propagation Neural Network
Author(s) -
Dunnan Liu,
Lingxiang Wang,
Hua Li,
Yuan Gao,
Xiaofeng Peng,
Mingguang Liu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/804/4/042045
Subject(s) - artificial neural network , electric vehicle , computer science , electrical load , scale (ratio) , grid , backpropagation , informatization , work (physics) , load balancing (electrical power) , electric network , automotive engineering , engineering , artificial intelligence , voltage , electrical engineering , telecommunications , mechanical engineering , power (physics) , physics , quantum mechanics , geometry , mathematics
The development of large-scale electric vehicles puts forward new requirements for safe and reliable operation of distribution network. Accurate and reliable EV load forecasting can effectively support large-scale EV load grid-connected operation and provide conditions for reasonable regulation of demand side resources. Firstly, this paper analyses the necessity and feasibility of electric vehicle load forecasting from the scale characteristics, regulation ability and informatization level of electric vehicle development. Then, a load forecasting method based on Back-Propagation neural network algorithm is proposed. Finally, the applicability of the model was verified based on the daily load of electric vehicles. The results show that the model can realize the effective load prediction of electric vehicles, and will support the smooth progress of related work.