PHOTOVOLTAIC POWER FORECASTING MODEL BASED ON AGGREGATE MODE DECOMPOSITION AND TCN-BiGRU

Li Mengyang; Chen Liu; Shi Meng; Zhao Yujiao

doi:10.19912/j.0254-0096.tynxb.2024-1799

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 91-99. DOI: 10.19912/j.0254-0096.tynxb.2024-1799

PHOTOVOLTAIC POWER FORECASTING MODEL BASED ON AGGREGATE MODE DECOMPOSITION AND TCN-BiGRU

Li Mengyang, Chen Liu, Shi Meng, Zhao Yujiao

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Abstract

To address the issue of low prediction accuracy caused by the strong randomness and high volatility of photovoltaic power generation, a hybrid prediction model based on aggregated mode decomposition（AMD）, temporal convolutional network （TCN） and bidirectional gated recurrent unit （BiGRU） is proposed. In view of the randomness and high volatility of photovoltaic power generation, a complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） is used to decompose the original photovoltaic sequence data for the first time, and a series of sub-sequences with different frequencies are obtained. Sample entropy （SE） is used to segment the molecular sequence, retaining the low-frequency and medium-frequency components of the signal. The high-frequency components obtained by CEEMDAN is decomposed secondarily by successive variational mode decomposition （SVMD） to reduce the sequence instability. Finally, the TCN-BiGRU model is used to predict each component, and final photovoltaic power prediction result is obtained by superimposing the prediction of each component. Experiments based on the analysis of arithmetic instances demonstrate that the forecasting accuracy and stability of the proposed hybrid prediction model outperform other approaches in photovoltaic power forecasting.

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photovoltaic power / prediction model / signal processing / aggregated mode decomposition / temporal convolutional network / bidirectional gated recurrent unit

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Li Mengyang, Chen Liu, Shi Meng, Zhao Yujiao. PHOTOVOLTAIC POWER FORECASTING MODEL BASED ON AGGREGATE MODE DECOMPOSITION AND TCN-BiGRU[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 91-99 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1799

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