RESEARCH OF SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON FOX-VMD COMBINED WITH WAVELET THRESHOLD DENOISING

Guo Wenkai; Wang Guo; Min Yongzhi; Su Pengfei; Liu Xinyue

doi:10.19912/j.0254-0096.tynxb.2024-2044

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 260-270. DOI: 10.19912/j.0254-0096.tynxb.2024-2044

RESEARCH OF SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON FOX-VMD COMBINED WITH WAVELET THRESHOLD DENOISING

Guo Wenkai, Wang Guo, Min Yongzhi, Su Pengfei, Liu Xinyue

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Abstract

Aiming the problems of noise interference,difficulty in extracting the information contained in PV power data and the error of prediction models, a short-term PV power prediction method based on optimization data processing and prediction error correction was proposed. Firstly, the correlation coefficients of meteorological features affecting PV power were calculated using the combination assignment method. Secondly, the parameters of the variational mode decomposition （VMD） were optimized using the FOX optimization algorithm, and the optimal parameters of the wavelet thresholding method （WT） were determined through experiments, completed data decomposition and denoising. Subsequently,a bidirectional long short-term memory （BiLSTM） model was constructed for each intrinsic mode function （IMF） component, and the initial prediction results were obtained by superposition reconstruction. Finally, an error prediction model based on the BiLSTM neural network was established, to obtain the final PV short-term power prediction results by using error compensation method. Example analyses demonstrated that the root mean square error （RMSE）, mean absolute error （MAE） and mean absolute percentage error （MAPE） values of the test set of the proposed method in this paper are 5.21 kW, 3.01 kW and 0.01%, respectively, which are 81.01%, 82.80%, and 88.89% lower than the original BiLSTM model, which means the method proposed in this paper can effectively extract the information, reduce the noise interference within the data, and mitigate the intrinsic error of the BiLSTM prediction model.

Key words

photovoltaic power generation / power forecasting / mode decomposition / wavelet threshold denoising / multi-stage predictive model

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Guo Wenkai, Wang Guo, Min Yongzhi, Su Pengfei, Liu Xinyue. RESEARCH OF SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON FOX-VMD COMBINED WITH WAVELET THRESHOLD DENOISING[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 260-270 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2044

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