SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON DATA AUGMENTATION AND OPTIMIZATION OF DHKELM

Guo Lijin; Ma Zongyang; Hu Xiaoyan

doi:10.19912/j.0254-0096.tynxb.2024-0693

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 463-471. DOI: 10.19912/j.0254-0096.tynxb.2024-0693

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON DATA AUGMENTATION AND OPTIMIZATION OF DHKELM

Guo Lijin^1,2, Ma Zongyang^1,2, Hu Xiaoyan^1,2

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Abstract

Addressing the issues of significant differences in data quality under different meteorological conditions and the high volatility in photovoltaic power which make it difficult to predict, a combined model is proposed that incorporates data augmentation （DA） and optimized deep hybrid kernel extreme learning machine （DHKELM）. At the outset, the spectral clustering algorithm is applied to categorize photovoltaic data based on varying meteorological conditions. Subsequently, the data set is expanded and its quality is enhanced by adding random noise of the same shape as the input data. Considering the numerous hyperparameters of DHKELM, a multi-strategy improved IPOA is proposed that integrates good point set initialization, golden sine update strategy, nonlinear perturbation, and adaptive perturbation of the optimal individual for hyperparameter optimization. Employing data from a photovoltaic station in Gonghe county photovoltaic park, Qinghai as a case study, the results demonstrate that the DA-IPOA-DHKELM model minimizes prediction errors under different weather and seasonal conditions and achieves fitting accuracy exceeding 90%, significantly enhancing the precision of photovoltaic power predictions and the applicability of the algorithm.

Key words

photovoltaic power / prediction / cluster analysis / data augmentation / deep hybrid kernel extreme learning machine / improved algorithm

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Guo Lijin, Ma Zongyang, Hu Xiaoyan. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON DATA AUGMENTATION AND OPTIMIZATION OF DHKELM[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 463-471 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0693

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