基于人工智能的太阳能系统光伏板性能增强深度学习模型

IF 2.1 4区 工程技术 Q3 CHEMISTRY, PHYSICAL International Journal of Photoenergy Pub Date : 2022-09-17 DOI:10.1155/2022/3437364
R. Meena, Ashutosh Kumar Singh, Shilpa Urhekar, RohitBhakar, N. K. Garg, Mohammad Israr, D. Kothari, C. Chiranjeevi, Prasath Srinivasan
{"title":"基于人工智能的太阳能系统光伏板性能增强深度学习模型","authors":"R. Meena, Ashutosh Kumar Singh, Shilpa Urhekar, RohitBhakar, N. K. Garg, Mohammad Israr, D. Kothari, C. Chiranjeevi, Prasath Srinivasan","doi":"10.1155/2022/3437364","DOIUrl":null,"url":null,"abstract":"This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossible to apply typical procedures in instances where data is unavailable. The new suggested artificial intelligence model employing multilayered perceptrons is employed for sizing solar systems, and this model functions on current photovoltaic modules that incorporate hybrid-sizing models; so, they should not be rejected entirely. In this work, the convergence speed of the proposed model for single diode, two diodes, and three diodes are the comparison factors to estimate the performance of the proposed model.","PeriodicalId":14195,"journal":{"name":"International Journal of Photoenergy","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems\",\"authors\":\"R. Meena, Ashutosh Kumar Singh, Shilpa Urhekar, RohitBhakar, N. K. Garg, Mohammad Israr, D. Kothari, C. Chiranjeevi, Prasath Srinivasan\",\"doi\":\"10.1155/2022/3437364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossible to apply typical procedures in instances where data is unavailable. The new suggested artificial intelligence model employing multilayered perceptrons is employed for sizing solar systems, and this model functions on current photovoltaic modules that incorporate hybrid-sizing models; so, they should not be rejected entirely. In this work, the convergence speed of the proposed model for single diode, two diodes, and three diodes are the comparison factors to estimate the performance of the proposed model.\",\"PeriodicalId\":14195,\"journal\":{\"name\":\"International Journal of Photoenergy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2022-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Photoenergy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/3437364\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Photoenergy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1155/2022/3437364","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

这项研究探讨了扩大太阳能系统规模的人工智能方法,如独立系统、并网系统和混合系统,以减轻环境影响。当提供了所有基本信息时,传统的尺寸确定方法可能是可行的替代方法。在数据不可用的情况下,不可能应用典型的程序。新提出的采用多层感知器的人工智能模型被用于确定太阳能系统的尺寸,并且该模型在包含混合尺寸模型的当前光伏模块上起作用;因此,它们不应该被完全拒绝。在这项工作中,所提出的单二极管、两个二极管和三个二极管模型的收敛速度是估计所提出模型性能的比较因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems
This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossible to apply typical procedures in instances where data is unavailable. The new suggested artificial intelligence model employing multilayered perceptrons is employed for sizing solar systems, and this model functions on current photovoltaic modules that incorporate hybrid-sizing models; so, they should not be rejected entirely. In this work, the convergence speed of the proposed model for single diode, two diodes, and three diodes are the comparison factors to estimate the performance of the proposed model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.00
自引率
3.10%
发文量
128
审稿时长
3.6 months
期刊介绍: International Journal of Photoenergy is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of photoenergy. The journal consolidates research activities in photochemistry and solar energy utilization into a single and unique forum for discussing and sharing knowledge. The journal covers the following topics and applications: - Photocatalysis - Photostability and Toxicity of Drugs and UV-Photoprotection - Solar Energy - Artificial Light Harvesting Systems - Photomedicine - Photo Nanosystems - Nano Tools for Solar Energy and Photochemistry - Solar Chemistry - Photochromism - Organic Light-Emitting Diodes - PV Systems - Nano Structured Solar Cells
期刊最新文献
IGWO-VINC Algorithm Applied to MPPT Strategy for PV System Numerical Evaluation and Artificial Neural Network (ANN) Model of the Photovoltaic Thermal (PVT) System with Different Nanofluids Enhancing CsSn0.5Ge0.5I3 Perovskite Solar Cell Performance via Cu2O Hole Transport Layer Integration Investigation of the Performance of a Sb2S3-Based Solar Cell with a Hybrid Electron Transport Layer (h-ETL): A Simulation Approach Using SCAPS-1D Software Maximizing Conversion Efficiency: A Numerical Analysis on P+ a-SiC/i Interface/n-Si Heterojunction Solar Cells with AMPS-1D
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1