基于地基云图的超短期太阳辐照预测方法与装置研究

张臻; 陈天鹏; 王磊; 邵玺; 张起源; 居秀丽

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

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (1) : 133-140. DOI: 10.19912/j.0254-0096.tynxb.2021-0912

基于地基云图的超短期太阳辐照预测方法与装置研究

张臻^1,2, 陈天鹏¹, 王磊^1,2, 邵玺¹, 张起源¹, 居秀丽²

作者信息 +

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

Author information +

文章历史 +

摘要

提出一种自主研发全天空成像仪的云图曝光优化方法,通过自主研发的地基云图采集仪采集地基云图,结合在同一时刻连续拍摄的不同曝光度的云图,利用动态范围优化算法对云图进行处理。对优化后的云图进行特征提取,将图像特征作为预测模型的输入数据,建立基于BP（back propagation）神经网络的预测模型。验证结果表明：在5 min预测尺度上,所建立模型的预测均方根误差相比持续性模型降低14.31%。与现有研究对比,所建立的模型具有更低的均方根误差（RMSE）。

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.

导出引用

张臻, 陈天鹏, 王磊, 邵玺, 张起源, 居秀丽. 基于地基云图的超短期太阳辐照预测方法与装置研究[J]. 太阳能学报. 2023, 44(1): 133-140 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0912

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

中图分类号： TK51

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