在机械通风教室中估计空气变化速率,使用单个CO2传感器和自动数据分割
Bowen Du1, Ibrahim Reda2, Dusan Licina1
1Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Environmental science & technology
|October 7, 2024
概括
本研究介绍了一种机器学习方法,使用二氧化碳 (CO2) 数据来估计课堂空气变化率 (ACH). 均衡方法显示了室内空气质量评估中最准确的ACH估计.
科学领域:
- 环境科学 环境科学
- 建筑科学 建筑科学
- 机器学习 机器学习
背景情况:
- 室内空气质量 (IAQ) 在学校至关重要,推动了有效监测的需要.
- 二氧化碳 (CO2) 监测越来越多地用于评估教室的通风和IAQ.
- 二氧化碳数据可以估计室外空气变化率 (ACH),影响健康,性能和能源使用.
研究的目的:
- 开发和应用一种新的机器学习方法来对二氧化碳时间序列数据进行细分.
- 用二氧化碳质量平衡原则来估计课堂ACH.
- 将新方法的 ACH 估计与传统的通风速率数据进行比较.
主要方法:
- 应用了一种机器学习算法,将二氧化碳时间序列划分为积累,平衡和衰减时期.
- 使用二氧化碳质量平衡方程来计算每个细分期的ACH.
- 收集了来自两个K-6学校的机械通风40个教室的数据.
主要成果:
- 该研究产生了每教室的多次每日ACH估计.
- 平衡方法提供了ACH估计最接近建筑自动化系统的估计.
- 与机械通风速率相比,衰变和积累方法显示了对ACH的轻微低估.
结论:
- 机器学习对二氧化碳数据的细分为IAQ评估提供了一个有希望的方法.
- 使用二氧化碳数据进行准确的 ACH 估计面临着挑战,原因是现实世界的变量,如占用率和空气混合.
- 需要进一步的研究来完善基于二氧化碳的ACH估计,以便可靠的IAQ监测.
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