Predicting the CO2 levels in buildings using deterministic and identified models

Pantazaras, Alexandros; Lee, Siew Eang; Santamouris, M.; Yang, Junjing

doi:10.1016/j.enbuild.2016.06.029

Cited by 52 publications

(32 citation statements)

References 33 publications

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“…Similar values of the MAPE coefficient were found by Pantazaras et al [31], who measured and simulated the concentration of CO2 inside of office buildings to estimate future levels of the contaminant. Basing the modelling on the occupancy schedule of the family members allowed the proper estimation of CO2 generation rates.…”

Section: Discussionsupporting

confidence: 67%

Indoor air quality modelling and measurements of a studio apartment with a mechanical exhaust system

Szczepanik-Ścisło

Flaga-Maryańczyk

2018

E3S Web Conf.

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Abstract. The aim of the research were measurements and an attempt to model the indoor air quality (IAQ) within a studio apartment with a mechanical exhaust system, manually controlled by the occupants. The authors based the modelling on the occupancy schedule of the family members and the recorded operation duration of the ventilation system. The purpose of the performed analysis was to answer the question if it is possible and to what extent to reflect numerically the conditions within the tested object. The authors studied also the carbon dioxide level in the context of controlling the system by the residents. The simulations were carried out using the CONTAM software, developed by NIST (National Institute of Standards and Technology).

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Section: Discussionsupporting

confidence: 67%

Indoor air quality modelling and measurements of a studio apartment with a mechanical exhaust system

Szczepanik-Ścisło

Flaga-Maryańczyk

2018

E3S Web Conf.

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“…Biswas described, that the artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data [49]. Pantazaras used incorporating CO 2 concentration as a factor in predictive models may unlock further optimization opportunities in controller applications, especially in buildings with highly varied occupancy, such as institutional buildings with the results, which suggest that there is indeed potential for at least short-term prediction using a very simple identification procedure [50]. Leung presents an investigation into the use of occupancy space electrical power demand to mimic occupants' activities in building cooling load prediction by intelligent approach, where the effect of individual behaviour on cooling load demand is less significant at building level than at office level and the proposed cooling demand prediction approach is able to predict daily peak loads satisfactory which would be useful for system dimensioning.…”

Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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“…Концентрация СО 2 в воздухе помещения является одним из ключевых факторов управления системами обеспечения микроклимата здания [2].…”

Section: моделирование влияния окон повышенной герметичности на теплоunclassified

“…Моделируются и мониторятся изменения основных параметров микроклимата: температура внутреннего воздуха, относительна влажность и концентрация углекислого газа СО 2 в воздухе. А потенциал энергосбережения в системах управления микроклиматом здания с ипользованием в качестве одного из основных параметров концентрацию СО 2 очень велик [2]. При этом набирают популярность исследования в области адаптивного теплового комфорта [3].…”

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Development and research of an intelectual power system for microclimate control in buildings

Kovalnogov¹,

Chamchiyan²

2016

Mod. Sci. Res. Ids. Rslt. Technol.

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Predicting the CO2 levels in buildings using deterministic and identified models

Cited by 52 publications

References 33 publications

Indoor air quality modelling and measurements of a studio apartment with a mechanical exhaust system

Indoor air quality modelling and measurements of a studio apartment with a mechanical exhaust system

Monitoring of the daily living activities in smart home care

Development and research of an intelectual power system for microclimate control in buildings

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