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首页> 外文期刊>Journal of Cleaner Production >A new ultra-short-term photovoltaic power prediction model based on ground-based cloud images
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A new ultra-short-term photovoltaic power prediction model based on ground-based cloud images

机译：基于地面云图像的超短期光伏发电新预测模型

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摘要

The cloud shading on the photovoltaic (PV) power station is one of the main factors that cause random changes in the PV output power, and thereby greatly influences an ultra-short-term photovoltaic power prediction. This paper presents an ultra-short-term prediction model for photovoltaic power generation based on dynamic characteristics of the cloud that is sheltering the sun. The proposed prediction model consists of three stages. In the first stage, the moving trajectory of the cloud is predicted using the motion vector and the cloud that shelters the sun is selected. In the second stage, the dynamic characteristics of target cloud, which have a great influence on the photovoltaic power generation, are extracted using the digital image processing. In the third stage, a prediction model based on the radial basis function (RBF) neural network, which is trained with processed sample data, is designed. Finally, the performance of RBF prediction model is compared with the performance of auto regressive (AR) model. The comparison shows that the power prediction accuracy of RBF model is 7.4% and the power prediction accuracy of AR model is 13.6%. The proposed ultra-short-term PV power prediction model can significantly improve the power prediction performance, especially in cloudy weather. (C) 2018 Elsevier Ltd. All rights reserved.
机译：光伏电站上的云影是导致光伏输出功率随机变化的主要因素之一，从而极大地影响了超短期光伏发电的预测。本文基于遮蔽太阳的云的动态特性，提出了一种光伏发电的超短期预测模型。所提出的预测模型包括三个阶段。在第一阶段，使用运动矢量预测云的运动轨迹，并选择遮蔽太阳的云。在第二阶段，使用数字图像处理提取对光伏发电有很大影响的目标云的动态特性。在第三阶段中，设计了基于径向基函数（RBF）神经网络的预测模型，该模型使用经过处理的样本数据进行训练。最后，将RBF预测模型的性能与自回归（AR）模型的性能进行了比较。比较表明，RBF模型的功率预测精度为7.4％，AR模型的功率预测精度为13.6％。所提出的超短期光伏功率预测模型可以显着提高功率预测性能，尤其是在多云天气下。 （C）2018 Elsevier Ltd.保留所有权利。

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