Predicting occupancy counts using physical and statistical Co2-based modeling methodologies

Zuraimi, M.S.; Pantazaras, Alexandros; Chaturvedi, Karn Ashokkumar; Yang, Junjing; Tham, Kwok Wai; Lee, Siew Eang

doi:10.1016/j.buildenv.2017.07.027

Cited by 108 publications

(54 citation statements)

References 33 publications

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“…The human emission rates of CO 2 (K occ ) reported in this research ranged between 13.56 L/h per person and 14.18 L/h per person. These values are close to previous studies that used stochastic methods to determine the parameters of a ventilation system (12.80 L/h) [19], and studies that used environmental chamber experiments to determine the CO 2 generation rate per person (12.60 L/h) [26]. However, the reported values were 21-24% lower than values assumed by other studies in the field of building ventilation with the same type of users (18.00-18.70 L/h) [16,27,28].…”

supporting

confidence: 84%

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

et al. 2018

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supporting

confidence: 84%

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

et al. 2018

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“…In this case it is evident the beneficial effect of the air conditioning system and of the air change, that permits to reduce both the temperature in the room and the CO2 concentration. The connection between the air exchange rates (AER) and the use of measured CO2 has been already evidenced in [13]. A summary of the results obtained in the various monitoring activities, in the different rooms, is provided in Tab.…”

Section: Co2 Air Concentration Campaigns: Results and Discussionmentioning

confidence: 91%

“…notebooks, smartphone chargers, various educational equipment) that significantly increase, and sometimes not easily predictable, the consumption of electricity. Moving from some recent trends in the research, in which the measurement of CO2 concentration has been recently proposed in the literature, for estimate the number of occupants [11][12][13], the paper try to analyse the perspectives of CO2 based prediction and evaluation of occupancy of building as an effective mean to obtain operational efficiency increase and of the quality of the environment. The step presented concern an attempt of obtaining a correlation between the CO2 concentration and some quantitative indicator starting from an experimental analysis carried out in university buildings.…”

Section: Introductionmentioning

confidence: 99%

CO2 Concentration and Occupancy Detection of Educational Buildings for Energy Efficiency Purposes: An Experimental Analysis

Franco¹,

Leccese²

2019

Preprint

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The problem of real-time estimation of occupancy of buildings (number of people in various zones at every time instant) is relevant to a number of emerging applications that achieve high energy efficiency through feedback control. The measurement of CO2 concentration can be considered an important indicator that allows to define the occupation of closed and crowded spaces. Interesting cases can be school buildings and other buildings used in civil and residential (shopping centres, hospitals, etc.). This paper, starting from an experimental analysis in different classrooms of a University campus in real operating conditions, in different period of the year, proposes a possible correlation between CO2 concentration and the occupancy profile of the spaces. The acquired data are used to present some graphical correlations and to understand the most important variables or combination of them. Starting from an accurate analysis of the data, attempts are made to define a preliminary estimation method through the development of a mathematical models of occupancy dynamics in a building, which show interesting results.

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“…In the case of the ANN, the number of hidden layers and hidden neurons are determined by parameter tuning. In reference to the previous studies conducted using the ANN (Dong et al [14]; Yang et al [13]; Ekwevugbe et al [37]; Yang et al [18]; Jiang et al [38]; Chen et al [11]; Zuraimi et al [39]; Li and Dong [40]), the parameters were determined through the Grid search by using 10-fold cross-validation on 10-50 units, with a hidden layer at a one or two units and the hidden neuron at a 10 unit. Table 6 shows the final results of the determined parameter values that show the highest accuracy.…”

Section: Selection Of Occupancy Estimation Algorithms and Parameter Tmentioning

confidence: 99%

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

et al. 2019

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To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: 1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and 2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

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Predicting occupancy counts using physical and statistical Co2-based modeling methodologies

Cited by 108 publications

References 33 publications

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

CO2 Concentration and Occupancy Detection of Educational Buildings for Energy Efficiency Purposes: An Experimental Analysis

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

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