Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health

Cao, Shi-Jie; Ding, Junwei; Ren, Cuiling

doi:10.1016/j.scs.2020.102190

Cited by 55 publications

(25 citation statements)

References 36 publications

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“…In other cases with room heights, spaces and ventilation modes which are different from the office, the performance of physical barrier and effective coverage distance of the outlet are also the focus of attention. Moreover, it is essential to integrate the prevention strategies (e.g., physical barrier) with related control techniques, such as multi-mode ventilation system ( Shao et al 2017 ), intelligent and online monitoring of air condition systems ( Cao and Ren 2018 , Cao and Ding 2020 ), demand-controlled supply air devices ( Wang et al 2021 ). Lastly, the design of physical barriers or air conditioning systems (including supply air openings and pipelines) has to be in line with functional and aesthetic requirements.…”

Section: Discussionmentioning

confidence: 99%

Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers

Ren

Wang

et al. 2021

Sustainable Cities and Society

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During the normalized phase of COVID-19, droplets or aerosol particles produced by infected personnel are considered as the potential source of infection with uncertain exposure risk. As such, in densely populated open spaces, it is necessary to adopt strategies to mitigate the risk of infection disease transmission while providing sufficient ventilation air. An example of such strategies is use of physical barriers. In this study, the impact of barrier heights on the spread of aerosol particles is investigated in an open office environment with the well-designed ventilation mode and supply air rate. The risk of infection disease transmission is evaluated using simulation of particle concentration in different locations and subject to a number of source scenarios. It was found that a barrier height of at least 60cm above the desk surface is needed to effectively prevent the transmission of viruses. For workstations within 4m from the outlet, a 70cm height is considered, and with a proper ventilation mode, it is shown that the barriers can reduce the risk of infection by 72%. However, for the workstations further away from the outlet (beyond 4m), the effect of physical barrier cannot be that significant. In summary, this study provides a theoretical analysis for implementing physical barriers, as a low-cost mitigation strategy, subject to various height scenarios and investigation of their effectiveness in reducing the infection transmission probability.

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Section: Discussionmentioning

confidence: 99%

Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers

Ren

Wang

et al. 2021

Sustainable Cities and Society

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“…Nevertheless, when using 3 or 4 sensors, there was an occasional error appearing in the prediction results, which had a great relationship to the sensor layout. Then, in order to achieve the optimal layout strategies with fewer sensors (e.g., 3 sensors), Cao et al (2020) investigated the systematic sensor deployment method by using clustering model of fuzzy C-means (FCM) algorithm for the efficient prediction of indoor environments. From the above, the well-behaved limited monitoring technology could be promising for the development of intelligent HVAC control system, which should be further incorporated into the comprehensive control strategies presented in this work.…”

Section: Discussionmentioning

confidence: 99%

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

2020

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Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

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“…Two [ 26 , 27 ] were excluded following EC4 as they were review papers or theoretical analysis (EC3, EC4). Furthermore, 20 studies [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] were removed due to missing information about the design methodology and the type of sensors used for measuring IAQ parameters (IC4, EC3). In addition, five more studies [ 48 , 49 , 50 , 51 , 52 ] were excluded as they were focused on thermal comfort parameters only or had no relevant details about IAQ sensors (IC2, IC3, EC3).…”

Section: Methodsmentioning

confidence: 99%

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

Saini

Dutta

Marques

2020

IJERPH

116

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Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

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Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health

Cited by 55 publications

References 36 publications

Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers

Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

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