利用机器学习模型来估计工业能源系统的能源节约

IF 12.4 Q1 ENVIRONMENTAL SCIENCES Resources Environment and Sustainability Pub Date : 2023-06-01 DOI:10.1016/j.resenv.2022.100103
Eva McLaughlin, Jun-Ki Choi
{"title":"利用机器学习模型来估计工业能源系统的能源节约","authors":"Eva McLaughlin,&nbsp;Jun-Ki Choi","doi":"10.1016/j.resenv.2022.100103","DOIUrl":null,"url":null,"abstract":"<div><p>Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100103"},"PeriodicalIF":12.4000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Utilizing machine learning models to estimate energy savings from an industrial energy system\",\"authors\":\"Eva McLaughlin,&nbsp;Jun-Ki Choi\",\"doi\":\"10.1016/j.resenv.2022.100103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.</p></div>\",\"PeriodicalId\":34479,\"journal\":{\"name\":\"Resources Environment and Sustainability\",\"volume\":\"12 \",\"pages\":\"Article 100103\"},\"PeriodicalIF\":12.4000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Resources Environment and Sustainability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666916122000470\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Environment and Sustainability","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666916122000470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 4

摘要

能源审计是减少能源使用、成本和碳排放的重要组成部分,但审计质量的差异取决于审计师,这可能会对能源评估的影响和可信度产生负面影响。本文收集了来自美国能源部赞助的研究项目的历史能源审计数据,并使用机器学习算法进行分析,以预测工业制造设施中压缩空气系统评估建议的需求节约。不同的能源审计员以不同的方式计算修复压缩空气系统泄漏的节省,因此在整个历史评估建议中,能源需求节省的计算方式不同。利用机器学习模型来提高现有实践的准确性,并减少由上述差异引起的变化。使用大量的历史评估推荐数据来训练五个独特的机器学习模型。设计了四种基本学习器模型和一种元学习器模型并进行了比较。结果表明,分布式随机森林模型对新情景下压缩空气能源需求节约的最佳预测误差在17%以内。这表明分布式随机森林模型可以更准确地量化修复压缩空气系统泄漏所节省的费用。此外,本研究的结果还深入了解了导致压缩空气系统泄漏的重要因素,以及为什么定期修复这些泄漏以节省资金和能源,同时减少排放至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Utilizing machine learning models to estimate energy savings from an industrial energy system

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.

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
Resources Environment and Sustainability
Resources Environment and Sustainability Environmental Science-Environmental Science (miscellaneous)
CiteScore
15.10
自引率
0.00%
发文量
41
审稿时长
33 days
期刊最新文献
Effects of asymmetric policies to achieve emissions reduction on energy trade: A North American perspective An efficient strategy to promote food waste composting by adding black soldier fly (Hermetia illucens) larvae during the compost maturation phase Household energy use and barriers in clean transition in the Tibetan Plateau Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning Unveiling driving disparities between satisfaction and equity of ecosystem services in urbanized areas
×
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