SHORT-TERM PHOTOVOLTAIC POWER GENERATION FORECAST BASED ON VMD-IAA-IHEKLM MODEL

Chen Long; Zhang Jing; Zhang Haoli; Ni Jianhui; Gao Dian

doi:10.19912/j.0254-0096.tynxb.2022-0843

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 135-141. DOI: 10.19912/j.0254-0096.tynxb.2022-0843

SHORT-TERM PHOTOVOLTAIC POWER GENERATION FORECAST BASED ON VMD-IAA-IHEKLM MODEL

Chen Long, Zhang Jing, Zhang Haoli, Ni Jianhui, Gao Dian

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Abstract

In order to improve the accuracy of photovoltaic power generation forecast, a short-term photovoltaic power generation forecast model based on variational mode decomposition（VMD）, improved archery algorithm（IAA） and improved extreme learning machine（ELM） was proposed. Firstly,decompose the photovoltaic data by variational modal decomposition algorithm Secondly ,use hybrid kernel to improve extreme learning Machine,and then use the Random opposition-based learning to improve archery algorithm. Finally, the algorithm is used to optimize the kernel function parameters. The accuracy of this method is verified by the data of DKA solar energy center in Australia.

Key words

photovoltaic power / power forecasting / machine learning / extreme learning machine / hybrid kernel / archery algorithm

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Chen Long, Zhang Jing, Zhang Haoli, Ni Jianhui, Gao Dian. SHORT-TERM PHOTOVOLTAIC POWER GENERATION FORECAST BASED ON VMD-IAA-IHEKLM MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 135-141 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0843

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