SHORT-TERM PHOTOVOLTATIC POWER FORECASTING BASED ON SVMD-IDBO-KELM

Wu Yanjuan; Rong Wang; Guo Yue; Ye Jisong

doi:10.19912/j.0254-0096.tynxb.2024-0292

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 271-279. DOI: 10.19912/j.0254-0096.tynxb.2024-0292

SHORT-TERM PHOTOVOLTATIC POWER FORECASTING BASED ON SVMD-IDBO-KELM

Wu Yanjuan^1-3, Rong Wang^1-3, Guo Yue⁴, Ye Jisong^1-3

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Abstract

To improve the accuracy of short-term photovoltaic power forecasting under various weather conditions,this paper introduces a model that combines Successive Variational Mode Decomposition （SVMD） and an Improved Dung Beetle Optimization algorithm （IDBO） to optimize a Kernel Extreme Learning Machine （KELM）. Firstly,the dataset is categorized into similar daily samples for different weather types using a Gaussian Mixture Model. Then,SVMD is applied to decompose the dataset into stable subsequences,thereby enhancing data quality. Next,IDBO is employed to optimize KELM,resulting in an IDBO-KELM prediction model specifically tailored for forecasting these subsequences. Finally,the predicted values from the subsequences are recombined to produce the final forecast. Experimental results demonstrate that this method performs well under three different weather conditions and exhibits superior accuracy when compared to other models.

Key words

photovoltaic power generation / predictive analytics / power prediction / KELM / SVMD / IDBO

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Wu Yanjuan, Rong Wang, Guo Yue, Ye Jisong. SHORT-TERM PHOTOVOLTATIC POWER FORECASTING BASED ON SVMD-IDBO-KELM[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 271-279 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0292

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