PREDICTION MODEL FOR MID- AND LONG-TERM PHOTOVOLTAIC POWER BASED ON INTEGRATION OF PCC-LSTM-XGBoost

Mi Wei; Jiang Xu; Chao Zibo; Pan Fengwen; Lei Yu

doi:10.19912/j.0254-0096.tynxb.2024-1883

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 155-164. DOI: 10.19912/j.0254-0096.tynxb.2024-1883

PREDICTION MODEL FOR MID- AND LONG-TERM PHOTOVOLTAIC POWER BASED ON INTEGRATION OF PCC-LSTM-XGBoost

Mi Wei, Jiang Xu, Chao Zibo, Pan Fengwen, Lei Yu

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Abstract

To address the limitations of traditional photovoltaic （PV） power prediction models in terms of forecasting accuracy and generalization capability, this paper designs and implements a prediction model that couples PCC, LSTM and XGBoost algorithms. Firstly, PCC is utilized to filter multi-dimensional features that influence PV power generation, thus constructing an optimized input feature set. Secondly, LSTM network is employed to model the long-term dependencies within the time series data, providing an initial power prediction. Concurrently, the XGBoost algorithm is introduced to model and predict the non-linear features of PV power generation. Finally, this paper lies in enhancing prediction accuracy and generalization capabilities by integrating the prediction results from LSTM and XGBoost models. Experimental results demonstrate that the proposed fusion model exhibits higher accuracy and stability in mid-and long-term PV power rolling forecasts, significantly outperforming traditional methods. Therefore, the proposed PCC-LSTM-XGBoost model offers a novel technical approach for accurate PV power prediction, making it particularly suitable for forecasting PV station power under various climatic conditions.

Key words

photovoltaic power generation / forecasting / neural networks / ensemble learning / LSTM / XGBoost / parameter extraction

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Mi Wei, Jiang Xu, Chao Zibo, Pan Fengwen, Lei Yu. PREDICTION MODEL FOR MID- AND LONG-TERM PHOTOVOLTAIC POWER BASED ON INTEGRATION OF PCC-LSTM-XGBoost[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 155-164 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1883

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