Machine Learning Based Problem Solving Approach in Green Computing

S. Bakre, A. Shiralkar, S. Shelar, Suchita Ingle
{"title":"Machine Learning Based Problem Solving Approach in Green Computing","authors":"S. Bakre, A. Shiralkar, S. Shelar, Suchita Ingle","doi":"10.1109/ESCI56872.2023.10099977","DOIUrl":null,"url":null,"abstract":"The issues related to conventional generation of electricity arethe matter of concern for power sector today. These include diminishing stock of coal over a period of time, unavailability of good quality coal, non-sustainable issues, ash handling problems etc. Green energy is the alternative to overcome these problems. The green energy is sustainable, renewable and economical. In India, the existing ratio of conventional to non-conventional generation as on 30th June 2022 is 72:28%. It is required to further improve this ratio to the tune of 60:40%. The performance of the green energy systems can be optimized by AI ML based green computing. Under the umbrella of AI, several technologies have been emerged. These technologies are machine learning, deep learning, data analytics, robotics, neural networks, expert systems, fuzzy logic systems, natural language processing, genetic algorithms etc. The green computing can be made more effective through research as regards how to use these technologies. In this paper, a novice techniques of AI ML based green computing have been proposed. Python programming language is used as a back end programming tool. The proposed methods are simple, cost effective and feasible.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESCI56872.2023.10099977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The issues related to conventional generation of electricity arethe matter of concern for power sector today. These include diminishing stock of coal over a period of time, unavailability of good quality coal, non-sustainable issues, ash handling problems etc. Green energy is the alternative to overcome these problems. The green energy is sustainable, renewable and economical. In India, the existing ratio of conventional to non-conventional generation as on 30th June 2022 is 72:28%. It is required to further improve this ratio to the tune of 60:40%. The performance of the green energy systems can be optimized by AI ML based green computing. Under the umbrella of AI, several technologies have been emerged. These technologies are machine learning, deep learning, data analytics, robotics, neural networks, expert systems, fuzzy logic systems, natural language processing, genetic algorithms etc. The green computing can be made more effective through research as regards how to use these technologies. In this paper, a novice techniques of AI ML based green computing have been proposed. Python programming language is used as a back end programming tool. The proposed methods are simple, cost effective and feasible.
绿色计算中基于机器学习的问题解决方法
与传统发电有关的问题是当今电力部门关注的问题。这些问题包括煤炭库存在一段时间内不断减少,优质煤炭供应不足,不可持续的问题,灰烬处理问题等。绿色能源是克服这些问题的替代选择。绿色能源具有可持续性、可再生性和经济性。在印度,截至2022年6月30日,现有的常规发电与非常规发电的比例为72:28%。需要进一步将这一比例提高到60:40%。基于人工智能机器学习的绿色计算可以优化绿色能源系统的性能。在人工智能的保护伞下,出现了几种技术。这些技术包括机器学习、深度学习、数据分析、机器人、神经网络、专家系统、模糊逻辑系统、自然语言处理、遗传算法等。通过研究如何使用这些技术,可以使绿色计算更加有效。本文提出了一种基于人工智能机器学习的绿色计算新技术。使用Python编程语言作为后端编程工具。所提出的方法简单、经济、可行。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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