ICEEMDAN-BiGRU-XGBoost-CrossAttention ULTRA-SHORT-TERM PV POWER PREDICTION TAKING INTO ACCOUNT SIMILARITY DAY AND ECM

Li Lianbing; Gao Yibo; Chen Ye; Dai Liangliang; Jing Ruixiong; Gao Guoqiang

doi:10.19912/j.0254-0096.tynxb.2024-1944

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 656-667. DOI: 10.19912/j.0254-0096.tynxb.2024-1944

ICEEMDAN-BiGRU-XGBoost-CrossAttention ULTRA-SHORT-TERM PV POWER PREDICTION TAKING INTO ACCOUNT SIMILARITY DAY AND ECM

Li Lianbing¹, Gao Yibo¹, Chen Ye¹, Dai Liangliang², Jing Ruixiong¹, Gao Guoqiang¹

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Abstract

To enhance the accuracy of photovoltaic power forecasting, this study proposes an ultra-short-term photovoltaic power prediction method incorporating similar-day selection and an Error Correction Model（ECM）. First, data is decomposed and reconstructed into high-frequency and low-frequency components using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （ICEEMDAN） method. which feeds into a feature extraction and prediction model combining CrossAttention-based Bidirectional Gated Recurrent Units（BiGRU） and eXtreme Gradient Boosting（XGBoost）. Second, the comprehensive similarity factor between the forecast day and historical days is calculated using grey correlation analysis. Meteorologically similar days for the forecast day are selected as input to the BiGRU-based similar-day information enhancement module. A residual forecast sequence is constructed based on the initial forecast sequence to build an error correction model using BiGRU. Finally, the prediction results from the ICEEMDAN-BiGRU-XGBoost-CrossAttention model, integrated with similar-day information, are combined with the prediction errors from the error correction model to derive the final intraday PV power prediction. Using actual meteorological and PV power generation data from photovoltaic stations, comparisons with different PV power generation models validate that the proposed method enhances the accuracy of intraday ultra-short-term PV power prediction and demonstrates practical application value.

Key words

photovoltaic power prediction / similar day selection / error correction / improved adaptive noise-complete ensemble empirical decomposition / combined model / CrossAttention

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Li Lianbing, Gao Yibo, Chen Ye, Dai Liangliang, Jing Ruixiong, Gao Guoqiang. ICEEMDAN-BiGRU-XGBoost-CrossAttention ULTRA-SHORT-TERM PV POWER PREDICTION TAKING INTO ACCOUNT SIMILARITY DAY AND ECM[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 656-667 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1944

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