基于分选的数据驱动型机器学习模型用于印度太阳辐射预报

IF 1.5 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Anuradha Munshi, R. M. Moharil
{"title":"基于分选的数据驱动型机器学习模型用于印度太阳辐射预报","authors":"Anuradha Munshi, R. M. Moharil","doi":"10.1007/s40998-024-00716-y","DOIUrl":null,"url":null,"abstract":"<p>Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.</p>","PeriodicalId":49064,"journal":{"name":"Iranian Journal of Science and Technology-Transactions of Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India\",\"authors\":\"Anuradha Munshi, R. M. Moharil\",\"doi\":\"10.1007/s40998-024-00716-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.</p>\",\"PeriodicalId\":49064,\"journal\":{\"name\":\"Iranian Journal of Science and Technology-Transactions of Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iranian Journal of Science and Technology-Transactions of Electrical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40998-024-00716-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Science and Technology-Transactions of Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40998-024-00716-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

能源是改善人类生活的主要驱动力。改善人类生活的所有活动都依赖于某种形式的能源。传统能源依赖于化石燃料,而化石燃料的储量有限,很快就会枯竭。另一方面,非常规/可再生能源是定期生产的,并且是清洁的,没有任何污染排放。这些能源包括太阳能、风能、水能、生物质能/生物燃气、地热能、潮汐能等。太阳能是印度等国家的主要能源之一,但它也有一些缺点,如初始成本高、依赖天气、存储成本高、需要空间等。因此,当务之急是建立准确的太阳辐射预测模型,以发现并解决这些问题。预报模型是根据每日或每小时的数据创建的,并针对具体地点。在这项工作中,提出了基于分选的机器学习模型,用于准确预测每小时的太阳辐射。这些模型是基于数据驱动的聚类模型。聚类是根据地理位置确定的。所提出的方法还有助于减少所需模型的数量,同时不影响高精度。在这项工作中,对从印度五个地理位置不同的站点收集到的全球和漫射太阳辐射数据进行了分析。对这些模型的验证表明其性能有所提高。与每日或每小时模型相比,所需的模型数量也大大减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India

Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India

Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.

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
5.50
自引率
4.20%
发文量
93
审稿时长
>12 weeks
期刊介绍: Transactions of Electrical Engineering is to foster the growth of scientific research in all branches of electrical engineering and its related grounds and to provide a medium by means of which the fruits of these researches may be brought to the attentionof the world’s scientific communities. The journal has the focus on the frontier topics in the theoretical, mathematical, numerical, experimental and scientific developments in electrical engineering as well as applications of established techniques to new domains in various electical engineering disciplines such as: Bio electric, Bio mechanics, Bio instrument, Microwaves, Wave Propagation, Communication Theory, Channel Estimation, radar & sonar system, Signal Processing, image processing, Artificial Neural Networks, Data Mining and Machine Learning, Fuzzy Logic and Systems, Fuzzy Control, Optimal & Robust ControlNavigation & Estimation Theory, Power Electronics & Drives, Power Generation & Management The editors will welcome papers from all professors and researchers from universities, research centers, organizations, companies and industries from all over the world in the hope that this will advance the scientific standards of the journal and provide a channel of communication between Iranian Scholars and their colleague in other parts of the world.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信