PHOTOVOLTAIC POWER INTERVAL PREDICTION METHOD BASED ON BAYESIAN OPTIMIZATION TO IMPROVE DEEP LEARNING MODEL HYPERPARAMETERS

Lu Jiaen; Zhao Jian; Li Xiaoyong

doi:10.19912/j.0254-0096.tynxb.2024-1938

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 644-655. DOI: 10.19912/j.0254-0096.tynxb.2024-1938

PHOTOVOLTAIC POWER INTERVAL PREDICTION METHOD BASED ON BAYESIAN OPTIMIZATION TO IMPROVE DEEP LEARNING MODEL HYPERPARAMETERS

Lu Jiaen¹, Zhao Jian¹, Li Xiaoyong²

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Abstract

To address the challenges to grid security posed by the randomness, intermittency, and fluctuations of photovoltaic （PV） power in PV power generation planning, an innovative deep learning model—the DRSTCG-GPR model based on fused spatiotemporal feature extraction—is proposed. This model fuses spatiotemporal features, introduces soft thresholding and attention mechanisms into the residual module, automatically optimizes hyperparameters via a Bayesian algorithm, and quantifies the uncertainty of predictions using Gaussian process regression, ultimately achieving high-precision interval prediction for short-term photovoltaic （PV） power.. Experimental comparisons show that, compared to traditional CNN, GRU, and CGRU models, this model exhibits significant advantages in point prediction accuracy （R² improvement of 2.20%）, interval prediction coverage performance （I_CP improvement of 2.10%）, and probabilistic prediction reliability.

Key words

photovoltaic power forecasting / deep learning / residual networks / temporal convolutional networks / Bayesian optimization / hyperparameter optimization / interval forecasting

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Lu Jiaen, Zhao Jian, Li Xiaoyong. PHOTOVOLTAIC POWER INTERVAL PREDICTION METHOD BASED ON BAYESIAN OPTIMIZATION TO IMPROVE DEEP LEARNING MODEL HYPERPARAMETERS[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 644-655 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1938

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