SHORT TERM PREDICTION OF PHOTOVOLTAIC POWER BASED ON IMPROVED SPARROW SEARCH ALGORITHM

Li Zheng; Luo Xiaorui; Zhang Jie; Cao Xin; Du Shenhui; Sun Hexu

doi:10.19912/j.0254-0096.tynxb.2022-0251

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 284-289. DOI: 10.19912/j.0254-0096.tynxb.2022-0251

SHORT TERM PREDICTION OF PHOTOVOLTAIC POWER BASED ON IMPROVED SPARROW SEARCH ALGORITHM

Li Zheng¹, Luo Xiaorui¹, Zhang Jie¹, Cao Xin², Du Shenhui¹, Sun Hexu¹

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Abstract

In order to improve the prediction accuracy of photovoltaic output power and ensure the optimal dispatching and stable operation of power grid, a photovoltaic output power prediction model based on improved sparrow search algorithm（SSA） is proposed. Firstly, the historical data collected by the experimental platform are analyzed to obtain the key climate influencing factors. Then, the empirical mode decomposition（EMD） and principal component analysis（PCA） are used to maintain the stability and reduce the dimension of the data. Thirdly, the BP neural network prediction model of improved sparrow search algorithm is established. Finally, an example is given to verify the model. The results show that the prediction model improves the convergence and divergence accuracy.

Key words

empirical mode decomposition / principal component analysis / improved sparrow search algorithm / short term prediction of photovoltaic output power

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Li Zheng, Luo Xiaorui, Zhang Jie, Cao Xin, Du Shenhui, Sun Hexu. SHORT TERM PREDICTION OF PHOTOVOLTAIC POWER BASED ON IMPROVED SPARROW SEARCH ALGORITHM[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 284-289 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0251

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