Warm, moderate, or cool-liker? A Benchmarking Framework to Characterize Occupant Overall Thermal Preferences based on Large-Scale Thermostat Data

恒温器 标杆管理 比例(比率) 热的 计算机科学 环境科学 汽车工程 工程类 机械工程 气象学 物理 经济 量子力学 管理
作者
Kai Chen,Ali Ghahramani
出处
期刊:Building and Environment [Elsevier BV]
卷期号:: 112046-112046
标识
DOI:10.1016/j.buildenv.2024.112046
摘要

Humans could exhibit distinct overall thermal preferences when exposed to identical indoor thermal environments, leading to distinct preference groups such as "warm-likers" or "cool-likers", who consistently prefer warmer or cooler conditions than the average population, respectively. Currently, most thermal comfort modelling studies focus on capturing momentary or instantaneous comfort states/preferences, ignoring the overall thermal preference. This paper proposes a benchmarking framework to identify and characterize overall thermal preferences based on preferred setpoint/outdoor temperature relationships derived from ECOBEE Donate Your Data program. Using descriptive statistics, we establish 3 temporally consistent overall preference groups, including warm-liker, moderate and cool-liker, along with a temporally chaotic preference group termed random. Our results demonstrate that warm-likers' preferred temperature setpoints are above 21.5°C on heating days and 24-25°C on cooling days, while cool-likers prefer setpoints below 19.6°C on heating days and 22°C on cooling days. We observed that around 50% of users exhibit secondary overall preferences, implying that overall thermal preference could change over time. On average, overall thermal preference can be established in 10 to 16 setpoint adjustments. The study reveals varied responses to outdoor temperature changes among users: many maintain constant indoor temperature preferences, while a significant number adjust their indoor temperatures upwards by 0.1°C to 0.4°C for each 1°C rise in outdoor temperature. A smaller group prefers cooler indoor temperatures as it gets warmer outside, showing a unique negative adjustment trend of -0.1. We also found that climate interacts with the overall preference group, with warmer climates having more warm-likers and vice versa.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
HThree完成签到 ,获得积分10
刚刚
1秒前
1秒前
1秒前
江城完成签到,获得积分10
1秒前
游01发布了新的文献求助10
2秒前
chengzugen发布了新的文献求助10
5秒前
5秒前
Altria完成签到,获得积分10
5秒前
赖炫芬发布了新的文献求助10
7秒前
隐形曼青应助peppa采纳,获得10
8秒前
shiyaouao发布了新的文献求助10
9秒前
HMSCC完成签到,获得积分10
9秒前
9秒前
毛驴发布了新的文献求助10
9秒前
10秒前
徐亚楠发布了新的文献求助10
10秒前
ding应助FunF采纳,获得10
13秒前
15秒前
科研通AI6.4应助暮叆采纳,获得10
15秒前
科研通AI6.3应助暮叆采纳,获得10
15秒前
科研通AI6.4应助暮叆采纳,获得10
15秒前
香蕉觅云应助暮叆采纳,获得10
15秒前
科研通AI6.3应助暮叆采纳,获得10
15秒前
英吉利25发布了新的文献求助10
15秒前
搜集达人应助暮叆采纳,获得10
16秒前
汉堡包应助暮叆采纳,获得10
16秒前
无花果应助Luo采纳,获得30
16秒前
科研通AI6.3应助暮叆采纳,获得10
16秒前
酷波er应助暮叆采纳,获得10
16秒前
17秒前
chengzugen完成签到,获得积分10
21秒前
香蕉觅云应助Lulu采纳,获得10
25秒前
oaker2021完成签到,获得积分10
26秒前
科研通AI6.3应助健忘涟妖采纳,获得10
26秒前
29秒前
zzzzzz完成签到,获得积分10
29秒前
32秒前
Silverexile发布了新的文献求助10
32秒前
CodeCraft应助白白采纳,获得10
33秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Nondestructive Testing Handbook: Vol. 4, Thermal and Infrared Testing (IR), 4th ed 800
作者名:Kristopher P. Plain,悉尼大学的,目前只能查到其四篇论文,想找到其博士论文 590
Évora na Idade Média 555
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Radical Reactions 500
Stratospheric Ozone: A Textbook 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7353868
求助须知:如何正确求助?哪些是违规求助? 8964879
关于积分的说明 19046738
捐赠科研通 7002243
什么是DOI,文献DOI怎么找? 3221808
关于科研通互助平台的介绍 2386204
邀请新用户注册赠送积分活动 2202542