SHORT-TERM PROBABILISTIC FORECASTING METHOD OF PHOTOVOLTAIC OUTPUT POWER BASED ON IMPROVED TEMPORAL CONVOLUTIONAL NETWORK

Xing Chen; Zhang Zhaobei

doi:10.19912/j.0254-0096.tynxb.2021-1033

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (2) : 373-380. DOI: 10.19912/j.0254-0096.tynxb.2021-1033

SHORT-TERM PROBABILISTIC FORECASTING METHOD OF PHOTOVOLTAIC OUTPUT POWER BASED ON IMPROVED TEMPORAL CONVOLUTIONAL NETWORK

Xing Chen, Zhang Zhaobei

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Abstract

In order to improve the accuracy of solarpower prediction, a short-term solarpower probability prediction method based on an improved temporal convolutional network is proposed. First, recursive feature elimination is used to determine the number of features, and the EGSG method is used for feature selection; then the variational mode decomposition（VMD） is used to decompose the power sequence. Finally, an improved time convolutional network prediction model combined with attention mechanism is constructed to obtain the predicted values at different quantiles in the future, the kernel density estimation is used to obtain the probability density curve. Experimental results show that the proposed method has higher prediction accuracy and can reflect the uncertainty of photovoltaic output moreeffectively.

Key words

photovoltaic output / prediction / probability density / VMD / attention mechanism

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Xing Chen, Zhang Zhaobei. SHORT-TERM PROBABILISTIC FORECASTING METHOD OF PHOTOVOLTAIC OUTPUT POWER BASED ON IMPROVED TEMPORAL CONVOLUTIONAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(2): 373-380 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1033

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