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Improved thermal comfort modeling for smart buildings: A data analytics study | |
2018-12 | |
发表期刊 | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
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ISSN | 0142-0615 |
卷号 | 103页码:634-643 |
发表状态 | 已发表 |
DOI | 10.1016/j.ijepes.2018.06.026 |
摘要 | Thermal comfort is a key consideration in the design and modeling of buildings and is one of the main steps to achieving smart building control and operation. Existing solutions model thermal comfort based on factors such as indoor temperature. However, these factors are not directly controllable by building operations, and instead are a by-product of complex interactions between controllable parameters such as air conditioning setpoint and other environmental conditions. In this paper, we use machine learning (ML) to bridge the gap between controllable building parameters and thermal comfort, by conducting an extensive study on the efficacy of different ML techniques for modeling comfort levels. We show that neural networks are especially effective, and achieve 98.7% accuracy on average. We also show these networks can lead to linear models where thermal comfort score scales linearly with the HVAC setpoint, and that the linear models can be used to quickly and accurately find the optimal setpoint for the desired comfort level. |
关键词 | Thermal comfort Machine learning Data analytics Smart buildings Smart city Smart grid |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000439746200060 |
出版者 | ELSEVIER SCI LTD |
EI入藏号 | 20182605358845 |
EI主题词 | Air conditioning ; Artificial intelligence ; Intelligent buildings ; Learning systems ; Smart city ; Thermal comfort |
EI分类号 | Buildings and Towers:402 ; Space Heating, Ventilation and Air Conditioning:643 ; Air Conditioning:643.3 ; Artificial Intelligence:723.4 |
WOS关键词 | MANAGEMENT ; INDOOR |
原始文献类型 | Article |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/27400 |
专题 | 信息科学与技术学院_PI研究组_范睿组 |
通讯作者 | Liu, Fang |
作者单位 | 1.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Wei,Liu, Fang,Fan, Rui. Improved thermal comfort modeling for smart buildings: A data analytics study[J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS,2018,103:634-643. |
APA | Zhang, Wei,Liu, Fang,&Fan, Rui.(2018).Improved thermal comfort modeling for smart buildings: A data analytics study.INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS,103,634-643. |
MLA | Zhang, Wei,et al."Improved thermal comfort modeling for smart buildings: A data analytics study".INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 103(2018):634-643. |
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