Wind Power Forecasting Based on Sparrow Search Algorithm and Kernel-Based Extreme Learning Machine

The accurate forecasting of wind power is necessary for the safe operation of power grid with the large-scale access of wind power. In this paper, a new forecasting method of wind power with high accuracy, based on sparrow search algorithm (SSA) and kernel-based extreme learning machine (KELM), was...

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Vydáno v:2024 8th International Conference on Power Energy Systems and Applications (ICoPESA) s. 575 - 579
Hlavní autoři: Hu, Mengying, Gao, Xiang, Duan, Jiandong, Shu, Chaoyang, Sun, Bin, Zhang, Jingyan, Song, Donghao, Chen, Bohao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.06.2024
Témata:
Accuracy
Correlation
Extreme learning machines
Forecasting
Kernel
kernel-based extreme learning machine
Power grids
Predictive models
sparrow search algorithm
Wind forecasting
wind power forecasting
Wind power generation
Wind speed
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Shrnutí:The accurate forecasting of wind power is necessary for the safe operation of power grid with the large-scale access of wind power. In this paper, a new forecasting method of wind power with high accuracy, based on sparrow search algorithm (SSA) and kernel-based extreme learning machine (KELM), was designed. Firstly, the correlation between wind power and wind speed was analyzed. Then, SSA was used to optimize the regularization coefficient and the kernel function parameter of KELM, and SSA-KELM forecasting model was constructed, using the wind speed as the input of the sample. The forecasting result of SSA-KELM was compared with that of KELM. It is proved that the wind power forecasting based on SSA-KELM is effective and its forecasting accuracy is higher than that of KELM model.
DOI:10.1109/ICOPESA61191.2024.10743948