PHOTOVOLTAIC POWER PREDICTION BASED ON CNN-BiLSTM-ROBUSTATTENTION

Yang Mengxue; Dai Zhiqiang; Zhu Yanyan; Liu Yongsheng

doi:10.19912/j.0254-0096.tynxb.2025-0466

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 475-482. DOI: 10.19912/j.0254-0096.tynxb.2025-0466

PHOTOVOLTAIC POWER PREDICTION BASED ON CNN-BiLSTM-ROBUSTATTENTION

Yang Mengxue^1,2, Dai Zhiqiang², Zhu Yanyan¹, Liu Yongsheng¹

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Abstract

A photovoltaic power prediction method based on CNN-BiLSTM and an improved robust attention mechanism （RobustAttention） is proposed to address the issues of model susceptibility to disturbances and poor stability. First,adaptive noise complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） is used to decompose historical power data into several intrinsic mode functions （IMF）,providing more stable data. Next,the IMFs and original data are input into the CNN-BiLSTM-RobustAttention model,where the local feature extraction ability of CNN and the long-term dependency correlation capturing ability of BiLSTM are combined to extract spatiotemporal features from the data. Finally,the features are input into the robust attention module to obtain the prediction results. Ablation experiments using historical power generation data from a photovoltaic power station in Jiangsu are conducted to validate the model’s prediction performance. The traditional attention mechanism is replaced with Robust Attention for comparison. The results show that the proposed method outperforms all models in terms of prediction performance across different time periods,with each component contributing to the improvement of model performance. RobustAttention can better capture anomalies in photovoltaic power generation data,further enhancing model stability and significantly improving prediction accuracy.

Key words

photovoltaic power prediction / neural network / deep learning / bidirectional long short-term memory / attention mechanism / convolutional neural network

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Yang Mengxue, Dai Zhiqiang, Zhu Yanyan, Liu Yongsheng. PHOTOVOLTAIC POWER PREDICTION BASED ON CNN-BiLSTM-ROBUSTATTENTION[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 475-482 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0466

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