AI-empowered online control optimization for enhanced efficiency and robustness of building central cooling systems

IF 13 Q1 ENERGY & FUELS
Lingyun Xie , Kui Shan , Hong Tang , Shengwei Wang
{"title":"AI-empowered online control optimization for enhanced efficiency and robustness of building central cooling systems","authors":"Lingyun Xie ,&nbsp;Kui Shan ,&nbsp;Hong Tang ,&nbsp;Shengwei Wang","doi":"10.1016/j.adapen.2025.100220","DOIUrl":null,"url":null,"abstract":"<div><div>Adopting Artificial Intelligence for optimizing building system controls has gained significant attention due to the growing emphasis on building energy efficiency. However, substantial gaps remain between academic research and the practical implementation of AI-based algorithms. Key factors hindering implementation include computational efficiency requirements and concerns about reliability in online applications. This paper addresses these challenges by presenting AI-empowered online control optimization technologies designed for practical implementation. A simplified deep learning-enabled Genetic Algorithm is developed to accelerate optimization processes, ensuring optimization intervals are short enough for online applications. This algorithm also significantly reduces CPU and memory usage, enabling deployment on miniaturized control station for field implementation. To enhance stability and reliability, a robust assurance scheme is introduced, which switches to expert knowledge-based control under abnormal conditions. Hardware-in-the-loop tests validate the proposed strategy's computation efficiency, control performance and operational robustness using a physical smart station controlling a simulated real-time dynamic cooling system. Test results show that the optimal control strategy achieves 7.66 % energy savings and exhibits strong operational robustness.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"18 ","pages":"Article 100220"},"PeriodicalIF":13.0000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666792425000149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

Adopting Artificial Intelligence for optimizing building system controls has gained significant attention due to the growing emphasis on building energy efficiency. However, substantial gaps remain between academic research and the practical implementation of AI-based algorithms. Key factors hindering implementation include computational efficiency requirements and concerns about reliability in online applications. This paper addresses these challenges by presenting AI-empowered online control optimization technologies designed for practical implementation. A simplified deep learning-enabled Genetic Algorithm is developed to accelerate optimization processes, ensuring optimization intervals are short enough for online applications. This algorithm also significantly reduces CPU and memory usage, enabling deployment on miniaturized control station for field implementation. To enhance stability and reliability, a robust assurance scheme is introduced, which switches to expert knowledge-based control under abnormal conditions. Hardware-in-the-loop tests validate the proposed strategy's computation efficiency, control performance and operational robustness using a physical smart station controlling a simulated real-time dynamic cooling system. Test results show that the optimal control strategy achieves 7.66 % energy savings and exhibits strong operational robustness.
人工智能支持的在线控制优化,提高了建筑中央冷却系统的效率和稳健性
由于对建筑节能的日益重视,采用人工智能来优化建筑系统控制得到了极大的关注。然而,学术研究和基于人工智能的算法的实际实施之间仍然存在巨大差距。阻碍实现的关键因素包括计算效率要求和对在线应用程序可靠性的关注。本文通过介绍为实际实施而设计的人工智能在线控制优化技术来解决这些挑战。开发了一种简化的深度学习遗传算法来加速优化过程,确保在线应用的优化间隔足够短。该算法还显著降低了CPU和内存的使用,使其能够部署在小型化的控制站上进行现场实施。为了提高稳定性和可靠性,引入了一个强大的保证方案,在异常情况下切换到基于专家知识的控制。硬件在环测试通过物理智能站控制模拟实时动态冷却系统验证了所提策略的计算效率、控制性能和操作鲁棒性。试验结果表明,最优控制策略节能7.66%,具有较强的鲁棒性。
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
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
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