Outdoor and Indoor Ozone Concentration Estimation Based on Artificial Neural Network and Single Zone Mass Balance Model

被引：7
作者：
Shen, Jialei [1 ]
Chen, Jie [1 ]
Zhang, Xinyi [1 ]
Zou, Sicong [1 ]
Gao, Zhi [1 ]
机构：
[1] Nanjing Univ, Sch Architecture & Urban Planning, 22 Hankou Rd, Nanjing 210093, Jiangsu, Peoples R China
来源：
10TH INTERNATIONAL SYMPOSIUM ON HEATING, VENTILATION AND AIR CONDITIONING, ISHVAC2017 | 2017年 / 205卷
关键词：
Ozone; Artificial neural network; I/O ratio; Deposition velocity; GREEN BUILDING-MATERIALS; DEPOSITION VELOCITIES; REACTION PROBABILITIES; SECONDARY EMISSIONS; SURFACE-REACTIONS; ACTIVITY PATTERN; REMOVAL; ALDEHYDES; AIR; CHEMISTRY;
D O I：
10.1016/j.proeng.2017.10.253
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Both outdoor and indoor ozone concentrations have negative health effects on human. This paper proposes an artificial neural network to estimate the outdoor ozone concentrations and a mass balance model tool to estimate indoor ozone concentrations. The prediction of outdoor and indoor ozone concentration levels is of great significance for people's health. The estimation models are validated by the measured data selected from the monitoring stations and field measurements in a room in Nanjing, respectively. The accuracy of the estimation models is evaluated. The neural network built in this paper can generally estimate the outdoor ozone concentrations to some extent, while the single zone mass balance model is useful for predicting indoor ozone concentration levels. (C) 2017 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：1835 / 1842
页数：8
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