SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON MODWT-CEEMDAN-TRANSFORMER-BiTCN

Tian Bo; Xiong Guojiang; Liu Yeyu; Wu Chenhao

doi:10.19912/j.0254-0096.tynxb.2025-0696

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 252-259. DOI: 10.19912/j.0254-0096.tynxb.2025-0696

SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON MODWT-CEEMDAN-TRANSFORMER-BiTCN

Tian Bo, Xiong Guojiang, Liu Yeyu, Wu Chenhao

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Abstract

This paper proposes a hybrid interval prediction model for Photovoltaic （PV） output by integrating the maximal overlap discrete wavelet transform （MODWT）, complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）, Bi-directional Temporal Convolutional Network （BiTCN） and Transformer architecture. Firstly, MODWT and CEEMDAN are applied to perform dual decomposition of the PV power time series, yielding intrinsic mode function components. Secondly, the IMFs are categorized into temporal and stochastic components based on their Spearman correlation coefficients. Subsequently, the temporal components are predicted using Transformer, while the stochastic components are predicted using BiTCN. The prediction results from both pathways are then reconstructed to obtain point forecasts. Finally, the point forecasts are expanded into prediction intervals using a kernel density estimation method. Validation on actual case studies demonstrates that this approach, leveraging dual signal decomposition （time and time-frequency domains） and integrating the dual-channel network architecture （BiTCN and Transformer）, achieves an average accuracy improvement of 29.36 % compared to other benchmark prediction models.

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photovoltaic power generation / deep learning / maximal overlap discrete wavelet transform / complete ensemble empirical mode decomposition with adaptive noise / Transformer / temporal convolutional networks

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Tian Bo, Xiong Guojiang, Liu Yeyu, Wu Chenhao. SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON MODWT-CEEMDAN-TRANSFORMER-BiTCN[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 252-259 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0696

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