REGIONAL PHOTOVOLTAIC OUTPUT DATA QUALITY ENHANCEMENT AND ULTRA-SHORT-TERM POWER PREDICTION METHOD BASED ON DGAT-TRANSFORMER ENSEMBLE ALGORITHM

Ren Hui; Yu Guangfa; Qiang Hanyue; Wang Fei; Zhen Zhao; Chang Xiqiang

doi:10.19912/j.0254-0096.tynxb.2024-2352

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 639-649. DOI: 10.19912/j.0254-0096.tynxb.2024-2352

REGIONAL PHOTOVOLTAIC OUTPUT DATA QUALITY ENHANCEMENT AND ULTRA-SHORT-TERM POWER PREDICTION METHOD BASED ON DGAT-TRANSFORMER ENSEMBLE ALGORITHM

Ren Hui¹, Yu Guangfa¹, Qiang Hanyue¹, Wang Fei¹, Zhen Zhao¹, Chang Xiqiang²

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Abstract

To address the decline in prediction accuracy caused by varying degrees of missing photovoltaic data across regional power stations, this paper proposes a novel framework that integrates photovoltaic power data imputation based on spatiotemporal correlation and dynamic graph attention network modeling with distributed photovoltaic power prediction leveraging a period-trend component decoupling approach. Specifically, a variational mode decomposition （VMD） model optimized via Bayesian algorithms is first employed to decompose the photovoltaic power series into multiple trend-stationary subsequences, which are then merged to form a comprehensive feature matrix. Subsequently, a hybrid model combining a dynamic graph attention network and Transformer is developed to more accurately capture the dynamic correlations within the feature matrix across spatiotemporal dimensions. This model is utilized to predict and reconstruct the missing subsequences, resulting in a complete photovoltaic dataset. Finally, a prediction model based on the principle of variational inference, termed the period-trend component decoupling model （LaST）, is introduced to further decompose the photovoltaic power into periodic and trend components. These components are individually modeled and predicted before being reconstructed to enhance overall prediction accuracy. Simulation results validate that the proposed method generates datasets with higher fidelity, which, when used as training samples, significantly improve prediction performance. Moreover, the superiority of the LaST model over conventional approaches in photovoltaic power forecasting is also demonstrated.

Key words

photovoltaic power generation / graph neural network / variational mode decomposition / Transformer / data accuracy / dynamic correlation

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Ren Hui, Yu Guangfa, Qiang Hanyue, Wang Fei, Zhen Zhao, Chang Xiqiang. REGIONAL PHOTOVOLTAIC OUTPUT DATA QUALITY ENHANCEMENT AND ULTRA-SHORT-TERM POWER PREDICTION METHOD BASED ON DGAT-TRANSFORMER ENSEMBLE ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 639-649 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2352

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