“… 33 Another recent study proposed an inverse modeling approach for estimating air change rate by searching for the air change rate that best explains the actual indoor CO 2 concentration and relative humidity trends. 34 These studies demonstrate the potential of using data-driven methods to understand building performance in general and air change rate in particular. However, the existing methods usually require prior knowledge about the environment and/or unmeasurable model parameters and thus work best for known contexts.…”
Section: Introductionmentioning
confidence: 76%
“…For example, Carrilho et al developed a novel signal processing approach based on the covariation of indoor and outdoor CO 2 concentration, which was then used by Alavy et al to estimate the year-long time-resolved air change rate . Another recent study proposed an inverse modeling approach for estimating air change rate by searching for the air change rate that best explains the actual indoor CO 2 concentration and relative humidity trends . These studies demonstrate the potential of using data-driven methods to understand building performance in general and air change rate in particular.…”
Low-cost air quality monitors are increasingly being
deployed in
various indoor environments. However, data of high temporal resolution
from those sensors are often summarized into a single mean value,
with information about pollutant dynamics discarded. Further, low-cost
sensors often suffer from limitations such as a lack of absolute accuracy
and drift over time. There is a growing interest in utilizing data
science and machine learning techniques to overcome those limitations
and take full advantage of low-cost sensors. In this study, we developed
an unsupervised machine learning model for automatically recognizing
decay periods from concentration time series data and estimating pollutant
loss rates. The model uses k-means and DBSCAN clustering to extract
decays and then mass balance equations to estimate loss rates. Applications
on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and
temporally. Further, detailed protocols were established to select
optimal model hyperparameters and filter out results with high uncertainty.
Overall, this model provides a novel solution to monitoring pollutant
removal rates with potentially wide applications such as evaluating
filtration and ventilation and characterizing indoor emission sources.
“… 33 Another recent study proposed an inverse modeling approach for estimating air change rate by searching for the air change rate that best explains the actual indoor CO 2 concentration and relative humidity trends. 34 These studies demonstrate the potential of using data-driven methods to understand building performance in general and air change rate in particular. However, the existing methods usually require prior knowledge about the environment and/or unmeasurable model parameters and thus work best for known contexts.…”
Section: Introductionmentioning
confidence: 76%
“…For example, Carrilho et al developed a novel signal processing approach based on the covariation of indoor and outdoor CO 2 concentration, which was then used by Alavy et al to estimate the year-long time-resolved air change rate . Another recent study proposed an inverse modeling approach for estimating air change rate by searching for the air change rate that best explains the actual indoor CO 2 concentration and relative humidity trends . These studies demonstrate the potential of using data-driven methods to understand building performance in general and air change rate in particular.…”
Low-cost air quality monitors are increasingly being
deployed in
various indoor environments. However, data of high temporal resolution
from those sensors are often summarized into a single mean value,
with information about pollutant dynamics discarded. Further, low-cost
sensors often suffer from limitations such as a lack of absolute accuracy
and drift over time. There is a growing interest in utilizing data
science and machine learning techniques to overcome those limitations
and take full advantage of low-cost sensors. In this study, we developed
an unsupervised machine learning model for automatically recognizing
decay periods from concentration time series data and estimating pollutant
loss rates. The model uses k-means and DBSCAN clustering to extract
decays and then mass balance equations to estimate loss rates. Applications
on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and
temporally. Further, detailed protocols were established to select
optimal model hyperparameters and filter out results with high uncertainty.
Overall, this model provides a novel solution to monitoring pollutant
removal rates with potentially wide applications such as evaluating
filtration and ventilation and characterizing indoor emission sources.
“…Therefore, the SF CMV is considered as not functional in the classroom in its initial configuration. As done previously by Xiong et al and Kam Dai and Chen, 28,29 the infiltration air flowrate through the envelope was estimated based on measurements of the temporal evolution of the CO 2 concentration in the room over a typical operating week.Figure 1.(a) Photo of the classroom serving as a demonstrator, (b) Reproduction of the classroom geometry in SketchUp software.…”
Section: Reference Thermal Zonementioning
confidence: 99%
“…Xion et al analyzed indoor CO 2 concentration and relative humidity trend data to estimate air infiltration. 28 The results showed that the relative humidity could not be considered to evaluate air infiltration due to moisture adsorption and desorption mechanisms of hygroscopic materials, but the CO 2 concentration values were very helpful to track the infiltration rate. The on-site measurements of time evolution of CO 2 concentration, after injecting an amount of CO 2 , performed by Kam Dai and Chen in residential units of Cheung Tai House in Taiwan, 29 allowed to estimate the air leakage, which was then used as an input to their numerical model.…”
Indoor air quality and heating energy consumption are closely linked, especially in densely occupied spaces, as classrooms. In a classroom of Rennes University, a transient thermal simulation highlights the impact of ventilation on heating consumptions for different cases: (1) without controlled mechanical ventilation (CMV), (2) with a single flow CMV (SF CMV) of a regulatory flow of 18 m3.h−1. person−1 during periods of occupancy, and (3) a double flow CMV (DF CMV) whose flow is during periods of occupancy either equal to 18 m3.h−1. person−1 or (4) to 35 m3.h−1.person−1. The infiltration flowrate was evaluated experimentally using CO2 concentration measurements in the first case, which corresponds to the actual situation. In the normal use of the classroom with 30 persons inside, CO2 concentration exceeds 5000 ppm and the heating energy consumption is equal to 52 kWh.m−2.year−1. In case (2) and (3), CO2 concentration has a maximum level of 1350 ppm and the energy consumption is equal to 65 and 53 kWh.m−2.year−1, respectively. In case (4), the maximum CO2 concentration stays in the range 800-900 ppm without excessive heating consumption (58 kWh.m−2.year−1). Finally, by limiting the fresh air flowrate to 18 m3.h−1.person−1 in DF CMV and coupling it to a façade insulation (extra Case 5), the heating consumption is reduced to 32 kWh.m−2.year−1. These results are valuable within the context of the upcoming energy renovation strategy of the campus. Practical application In most schools, in France and elsewhere, insufficient ventilation flowrates are often observed compared to regulatory requirements. If compliance with ventilation standards and the subsequent revision of CMV installations are necessary to ensure the health of users, it is important to evaluate the energy cost of these renovations, and to identify the best choice to reduce heating consumptions. Our contribution is to compare different renovation choices in terms of two indicators: energy consumption and indoor air quality evaluated using CO2 concentration.
“…However, even if the ESS container is sealed, it is very difficult to block air inflow from the outside completely. Xiong et al [ 17 ] analyzed office air permeability using CO 2 concentration and humidity ratio data, while Tanyer et al [ 18 ] investigated the airtightness performance and consequent energy efficiencies of four types of container houses (CHs). Although the airtightness of the CH junction was improved, it was confirmed that the condensation problem still occurred.…”
The implementation of an energy storage system (ESS) as a container-type package is common due to its ease of installation, management, and safety. The control of the operating environment of an ESS mainly considers the temperature rise due to the heat generated through the battery operation. However, the relative humidity of the container often increases by over 75% in many cases because of the operation of the air conditioner which pursues temperature-first control. Humidity is a major factor which can cause safety issues such as fires owing to insulation breakdown caused by condensation. However, the importance of humidity control in ESS is underestimated compared to temperature control. In this study, temperature and humidity monitoring and management issues were addressed for a container-type ESS by building sensor-based monitoring and control systems. Furthermore, a rule-based air conditioner control algorithm was proposed for temperature and humidity management. A case study was conducted to compare the conventional and proposed control algorithms and verify the feasibility of the proposed algorithm. The results showed that the proposed algorithm reduced the average humidity by 11.4% compared to the value achieved with the existing temperature control method while also maintaining the temperature.
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