ULTRA-SHORT-TERM PV POWER PREDICTION BASED ON CAUSAL FEATURE EXTRACTION AND IMPROVED Q-L ALGORITHM

Zhang Li; Liu Jiawei; Sun Shuyan; Zhang Tao; Zhang Hongwei

doi:10.19912/j.0254-0096.tynxb.2025-0571

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 188-200. DOI: 10.19912/j.0254-0096.tynxb.2025-0571

ULTRA-SHORT-TERM PV POWER PREDICTION BASED ON CAUSAL FEATURE EXTRACTION AND IMPROVED Q-L ALGORITHM

Zhang Li^1,2, Liu Jiawei¹, Sun Shuyan¹, Zhang Tao^1,3, Zhang Hongwei⁴

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Abstract

To address the issues of redundant meteorological features, insufficient multi-timescale dynamic response, and lack of dynamic adaptability in ensemble forecasting methods for photovoltaic power prediction, this paper proposes an ultra-short-term prediction model based on causal feature extraction and an improved Q-L algorithm. First, a causal feature extraction method based on entropy calculation is adopted to screen key meteorological factors. This method can reflect nonlinear coupling relationships and improve feature selection quality. Second, a parallel prediction model combining a bidirectional long short-term memory network （BiLSTM） and a TCN-Transformer is constructed to capture short-term temporal fluctuations, local patterns, and long-term dependencies, achieving multi-scale feature fusion. Third, an improved Q-L algorithm with a dynamic reward-punishment mechanism is proposed to adaptively adjust the prediction weights of each model through iterative feedback and produce the final weighted prediction result. Finally, multiple simulation validations are carried out under three typical weather conditions. Comparative experimental results show that the proposed model achieves stable prediction performance. Under rainy conditions, compared with a single BiLSTM model, the E_RMSE and E_MAE metrics decrease by an average of 49.3% and 51.9%, respectively, indicating improved prediction accuracy and generalization ability.

Key words

photovoltaic power / feature extraction / reinforcement learning / long short-term memory network / TCN-Transformer / Q-L algorithm

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Zhang Li, Liu Jiawei, Sun Shuyan, Zhang Tao, Zhang Hongwei. ULTRA-SHORT-TERM PV POWER PREDICTION BASED ON CAUSAL FEATURE EXTRACTION AND IMPROVED Q-L ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 188-200 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0571

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