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28 July 2022 Design and implementation of predictive model for urban new energy charging equipment based on genetic algorithm optimized BP neural network
Lei Zhang
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Proceedings Volume 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022); 123032V (2022) https://doi.org/10.1117/12.2642687
Event: International Conference on Cloud Computing, Internet of Things, and Computer Applications, 2022, Luoyang, China
Abstract
The popularity of electric vehicles has brought great challenges to the deployment of urban charging equipment. Whether the construction process of charging equipment can meet the charging needs of new energy vehicles in cities affects the attitude of potential customers towards new energy vehicles and is directly related to the advancement of the national carbon neutrality process. Aiming at this problem, this paper establishes a new energy charging equipment prediction model based on BP neural network optimized by genetic algorithm. The model predicts the number of urban charging equipment matching electric vehicles based on historical data. The results show that the BP neural network optimized by the genetic algorithm can achieve convergence faster than the single BP neural network, and the error is smaller, which proves the feasibility of the model.
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Lei Zhang "Design and implementation of predictive model for urban new energy charging equipment based on genetic algorithm optimized BP neural network", Proc. SPIE 12303, International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), 123032V (28 July 2022); https://doi.org/10.1117/12.2642687
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KEYWORDS
Neural networks
Data modeling
Genetic algorithms
Genetics
Statistical modeling
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