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1 August 2022 Electric vehicle charging load prediction with improved Kernel Extreme Learning Machine
Xin Wang
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Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122572O (2022) https://doi.org/10.1117/12.2640201
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China
Abstract
The prediction of electric vehicle charging load can provide an important basis for power grid planning and design. Aiming at the problem that the existing methods are insufficient in the prediction accuracy of electric vehicle charging load, a Kernel Extreme Learning Machine (PSO-KELM) electric vehicle charging load prediction model based on Particle Swarm Optimization (PSO) algorithm optimization is proposed. The model adopts the Monte Carlo method to simulate the electric vehicle charging load for a week by considering the electric vehicle ownership, charging mode, initial charging time and driven distance, and uses PSO-KELM to predict the charging load. Experiments show that the prediction model can comprehensively consider the influencing factors of charging load, effectively improve the accuracy of electric vehicle charging load prediction, and provide a reference for the optimal operation and planning of the power grid.
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Xin Wang "Electric vehicle charging load prediction with improved Kernel Extreme Learning Machine", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122572O (1 August 2022); https://doi.org/10.1117/12.2640201
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KEYWORDS
Data modeling
Monte Carlo methods
Particle swarm optimization
Optimization (mathematics)
Algorithms
Statistical modeling
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