Research on photovoltaic power prediction method based on improved extreme learning machine

Chao Lei; Yuan Xie

doi:10.1117/12.2689385

10 August 2023 Research on photovoltaic power prediction method based on improved extreme learning machine

Chao Lei, Yuan Xie

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274845 (2023) https://doi.org/10.1117/12.2689385
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

The power of photovoltaic generation fluctuates greatly under the influence of weather conditions, so accurate prediction is beneficial to the management and dispatching of power grid. In this paper, pearson correlation coefficient method is used to analyze the factors affecting the photovoltaic power generation. the features with high correlation are selected as input variables. A hybrid kernel extreme learning machine model optimized by particle swarm optimization algorithm is proposed. The parameters in the model are optimized. Based on the model, the data was divided into four seasons. The prediction was carried out. The results showed that the PSO-HKELM model had a high power prediction accuracy for different seasons, so as to realize the effective prediction of photovoltaic power generation.

Citation Download Citation

Chao Lei and Yuan Xie "Research on photovoltaic power prediction method based on improved extreme learning machine", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274845 (10 August 2023); https://doi.org/10.1117/12.2689385

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