Jie Fan , Lei Wang , Zhen Zhang , Ming Liu , Xinyue Cao , Min Gong , Qiuping Tang , Chao She , Fang Qi , Hucheng Si , Dan Song , Qiyuan Zhang , Peng Xie
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引用次数: 0
摘要
从全球水平辐照度(GHI)中获取高精度漫射辐照度可为光伏系统的设计、运行和维护提供全面有效的数据。本研究在人工神经网络(ANN)模型中加入了云特征,以提高 1 分钟分辨率数据集上漫射分量的估算精度。云特征是利用图像处理算法从地面云图像中提取的,包括光谱特征、纹理特征和云覆盖率。经过数据验证,包含所有云特征的 ANN 模型的归一化均方根误差(NRMSE)为 17.1%,与基本 ANN 模型相比降低了 13%。基于适当时间尺度的数据平均提高了约 2% 的精度;天气分类和云分类在某些情况下都提高了 10% 以上的精度,但其他情况下可能会由于某些原因而恶化,这需要进一步研究。
Approaches to improve the accuracy of estimating the diffuse fraction of 1-min resolution global horizontal irradiance using cloud images
Obtaining high-precision diffuse irradiance from global horizontal irradiance (GHI) can serve comprehensive and effective data for PV system design, operation and maintenance. This study has incorporated cloud features in an artificial neural network (ANN) model to improve the estimation accuracy of diffuse fraction on 1-min resolution dataset. The cloud features are extracted from ground-based cloud images, including spectrum features, texture features and cloud cover ratio, with image processing algorithms. After data validation, the ANN model which incorporated all the cloud features has achieved a normalized root mean square error (NRMSE) of 17.1 %, representing a 13 % reduction compared to the basic ANN model, we have investigated additional strategies that further optimize the model performance, including cloud classification, weather classification and data averaging, and quantified the effects of the proposed approaches based on actual station data. The data averaging based on proper time scale has brought about 2 % in accuracy improvement; the weather classification and cloud classification have both brought above 10 % of accuracy improvement in some cases but others may deteriorate for some reasons that need to be further investigated, based on this, we have analyzed and summarized the deficiencies in our research and proposed detailed research directions for future endeavors.
期刊介绍:
Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices.
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