SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY CLUSTERING AND PCC-VMD-SSA-KELM MODEL

Li Zheng; Zhang Jie; Xu Ruosi; Luo Xiaorui; Mei Chunxiao; Sun Hexu

doi:10.19912/j.0254-0096.tynxb.2022-1608

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 460-468. DOI: 10.19912/j.0254-0096.tynxb.2022-1608

SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY CLUSTERING AND PCC-VMD-SSA-KELM MODEL

Li Zheng¹, Zhang Jie¹, Xu Ruosi¹, Luo Xiaorui¹, Mei Chunxiao², Sun Hexu¹

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Abstract

Because the randomness and instability of photovoltaic power generation will affect the accuracy of power prediction, this paper proposes a short-term photovoltaic power prediction model based on Pearson correlation coefficient （PCC）, K-means algorithm （K-means）, variational mode decomposition （VMD）, sparrow search algorithm （SSA）, and kernel based extreme learning machine （KELM）. Firstly, PCC is used to select the main factors as input; K-means algorithm clusters the historical data into sunny, cloudy and rainy days. Secondly, VMD decomposes the original signal to fully extract the input factor information in the set to improve the data quality. SSA optimizes the kernel function parameters and regularization coefficients of KELM model to solve its sensitive problem of parameter selection. Finally, the final prediction result is obtained by superimposing the prediction values of different series. The simulation results show that the PCC-VMD-SSA-KELM model with similar day clustering has small prediction error.

Key words

photovoltaic power generation / power forecasting / variational mode decomposition / K-means / sparrow search algorithm / kernel based extreme learning machine

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Li Zheng, Zhang Jie, Xu Ruosi, Luo Xiaorui, Mei Chunxiao, Sun Hexu. SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SIMILAR DAY CLUSTERING AND PCC-VMD-SSA-KELM MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 460-468 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1608

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