Estimating PV Soiling Loss Using Panel Images and a Feature-Based Regression Model

IF 2.5 3区工程技术 Q3 ENERGY & FUELS IEEE Journal of Photovoltaics Pub Date : 2024-03-02 DOI:10.1109/JPHOTOV.2024.3388168

Mingda Yang;Wasim Javed;Bing Guo;Jim Ji

{"title":"Estimating PV Soiling Loss Using Panel Images and a Feature-Based Regression Model","authors":"Mingda Yang;Wasim Javed;Bing Guo;Jim Ji","doi":"10.1109/JPHOTOV.2024.3388168","DOIUrl":null,"url":null,"abstract":"Solar energy from solar photovoltaics (PV) has become a rapidly growing sustainable energy source around the world. However, maintaining PV system efficiency remains a challenging problem. In desert regions, soiling is one of the most significant environmental factors that can cause PV system loss. In our early work, a PV soiling loss estimation method based on a single-image feature and in-lab testing was developed. In this study, we extend our previous work by incorporating various image features in a machine-learning regression model to predict PV soiling loss. The new model is trained and tested using PV performance data and RAW panel images collected in the field over several months, covering real-time soiling loss levels up to about 28%. There are 479 RAW images with 21 unique soiling loss levels, which were taken under different camera settings. The results show that the new method can reliably predict the soiling loss when the images are taken under similar settings as the training data (\n<italic>R</i>\n-squared value of 0.98 and normalized RMSE is 0.01 for the training dataset).","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 4","pages":"661-668"},"PeriodicalIF":2.5000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Photovoltaics","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10517757/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

Solar energy from solar photovoltaics (PV) has become a rapidly growing sustainable energy source around the world. However, maintaining PV system efficiency remains a challenging problem. In desert regions, soiling is one of the most significant environmental factors that can cause PV system loss. In our early work, a PV soiling loss estimation method based on a single-image feature and in-lab testing was developed. In this study, we extend our previous work by incorporating various image features in a machine-learning regression model to predict PV soiling loss. The new model is trained and tested using PV performance data and RAW panel images collected in the field over several months, covering real-time soiling loss levels up to about 28%. There are 479 RAW images with 21 unique soiling loss levels, which were taken under different camera settings. The results show that the new method can reliably predict the soiling loss when the images are taken under similar settings as the training data ( R -squared value of 0.98 and normalized RMSE is 0.01 for the training dataset).

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用面板图像和基于特征的回归模型估算光伏污损损失

太阳能光伏发电（PV）已成为全球快速增长的可持续能源。然而，保持光伏系统的效率仍然是一个具有挑战性的问题。在沙漠地区，污损是导致光伏系统损耗的最主要环境因素之一。在我们早期的工作中，开发了一种基于单图像特征和实验室测试的光伏污损估计方法。在本研究中，我们扩展了之前的工作，将各种图像特征纳入机器学习回归模型，以预测光伏污损。我们使用光伏性能数据和几个月来在现场收集的 RAW 面板图像对新模型进行了训练和测试，涵盖了最高约 28% 的实时污损水平。共有 479 张 RAW 图像，21 个独特的污损等级，这些图像是在不同的相机设置下拍摄的。结果表明，当图像是在与训练数据相似的设置下拍摄时，新方法可以可靠地预测脏污损失（训练数据集的 R 平方值为 0.98，归一化 RMSE 为 0.01）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

IEEE Journal of Photovoltaics ENERGY & FUELS-MATERIALS SCIENCE, MULTIDISCIPLINARY

CiteScore

7.00

自引率

10.00%

发文量

206

期刊介绍： The IEEE Journal of Photovoltaics is a peer-reviewed, archival publication reporting original and significant research results that advance the field of photovoltaics (PV). The PV field is diverse in its science base ranging from semiconductor and PV device physics to optics and the materials sciences. The journal publishes articles that connect this science base to PV science and technology. The intent is to publish original research results that are of primary interest to the photovoltaic specialist. The scope of the IEEE J. Photovoltaics incorporates: fundamentals and new concepts of PV conversion, including those based on nanostructured materials, low-dimensional physics, multiple charge generation, up/down converters, thermophotovoltaics, hot-carrier effects, plasmonics, metamorphic materials, luminescent concentrators, and rectennas; Si-based PV, including new cell designs, crystalline and non-crystalline Si, passivation, characterization and Si crystal growth; polycrystalline, amorphous and crystalline thin-film solar cell materials, including PV structures and solar cells based on II-VI, chalcopyrite, Si and other thin film absorbers; III-V PV materials, heterostructures, multijunction devices and concentrator PV; optics for light trapping, reflection control and concentration; organic PV including polymer, hybrid and dye sensitized solar cells; space PV including cell materials and PV devices, defects and reliability, environmental effects and protective materials; PV modeling and characterization methods; and other aspects of PV, including modules, power conditioning, inverters, balance-of-systems components, monitoring, analyses and simulations, and supporting PV module standards and measurements. Tutorial and review papers on these subjects are also published and occasionally special issues are published to treat particular areas in more depth and breadth.

期刊最新文献

2024 Index IEEE Journal of Photovoltaics Vol. 14 Design and Development of a New Smart Portable I-V Tracer Table of Contents Front Cover TechRxiv: Share Your Preprint Research with the World!