RESEARCH ON ULTRA-SHORT-TERM SOLAR IRRADIANCE PREDICTION METHOD AND DEVICEBASED ON GROUND-BASED CLOUD IMAGES

Zhang Zhen; Chen Tianpeng; Wang Lei; Shao Xi; Zhang Qiyuan; Ju Xiuli

doi:10.19912/j.0254-0096.tynxb.2021-0912

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (1) : 133-140. DOI: 10.19912/j.0254-0096.tynxb.2021-0912

RESEARCH ON ULTRA-SHORT-TERM SOLAR IRRADIANCE PREDICTION METHOD AND DEVICEBASED ON GROUND-BASED CLOUD IMAGES

Zhang Zhen^1,2, Chen Tianpeng¹, Wang Lei^1,2, Shao Xi¹, Zhang Qiyuan¹, Ju Xiuli²

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Abstract

This paper proposes a cloud image exposure optimization method based on the all-sky imager, the ground-based cloud map is collected by the self-developed ground-based cloud map collection instrument, combined with cloud images with different exposures continuously shot at the same time, the cloud images are processed using dynamic range optimization algorithms. Perform feature extraction on the optimized cloud image, use image features as the input data of the prediction model, and establish a prediction model based on BP neural network. The verification results show that on the 5 min prediction scale, compared with the persistent model, the root mean square error of the established model is reduced by 14.31%. Compared with the existing research, the model established in this paper has a lower E_RMSE.

Key words

solar energy / irradiance prediction / ground-based cloud image / image processing / machine learning

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Zhang Zhen, Chen Tianpeng, Wang Lei, Shao Xi, Zhang Qiyuan, Ju Xiuli. RESEARCH ON ULTRA-SHORT-TERM SOLAR IRRADIANCE PREDICTION METHOD AND DEVICEBASED ON GROUND-BASED CLOUD IMAGES[J]. Acta Energiae Solaris Sinica. 2023, 44(1): 133-140 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0912

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