.png){kind=icon}
返回
Carbon dioxide-based occupancy estimation using stochastic differential equations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apenergy.2018.11.078.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the existing building stock, heating, cooling and ventilation usually run on fixed schedules, in many cases, even all day. In particular, ventilation systems often run with a constant air flow rate that is adjusted based on the assumption of maximum occupancy. Hence, reducing the operation to the required extent would offer energy potential. Model-based, demand-controlled heating, ventilation and air-conditioning systems can help to achieve this. Information on the number of occupants present in a room and ventilation-related quantities, such as the room-air change rate, are important parameters to control the ventilation of a building. Hence, an automated estimation of these would help to find optimal model-based control strategies. In this work, the use of a grey-box model based on a carbon dioxide mass balance is explored to estimate room occupancy and ventilation parameters. The main contribution of this study is the employment of stochastic differential equations to describe this mass balance. In contrast to ordinary differential equations, the stochastic framework employed here is able to address measurement errors as well as errors that derive from an inevitably oversimplified description of the physical system. Due to its probabilistic nature, this approach inherently includes a method of parameter estimation using the maximum likelihood approach, which additionally provides a measure of uncertainty for every estimated parameter. The presented model was tested in one naturally ventilated and one mechanically ventilated office room. In both cases, the estimation of occupancy and of the model parameters showed promising results. This leads to the conclusion that the suggested model can be considered as a candidate to be integrated into building control systems.
Keyword:
Occupancy estimation
Occupant behaviour
Predictive control
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11
论文数:
2.6W
被引数:
17.8W
机构
引用论文
Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach.png)
MEASUREMENT
OAAI
MEASUREMENTIF5.6
OAAI分享收藏A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building基于隐马尔可夫模型的占用检测方法和低能耗住宅的案例研究ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏分享收藏Gene diversity, agroecological structure and introgression patterns among village chicken populations across North, West and Central AfricaBMC Genetics
OAAI
BMC GeneticsIF0
OAAI分享收藏Three unobtrusive domestic occupancy measurement technologies under qualitative reviewENERGY AND BUILDINGSOAAI
ENERGY AND BUILDINGSIF7.1
OAAI分享收藏Predictive control of indoor environment using occupant number detected by video data and CO2 concentration利用视频数据检测的乘员数量和CO2浓度对室内环境进行预测控制ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏
查看更多内容