Indoor occupancy estimation from carbon dioxide concentration

Jiang, Chaoyang; Masood, Mustafa K.; Soh, Yeng Chai; Li, Hua

doi:10.1016/j.enbuild.2016.09.002

Cited by 169 publications

(104 citation statements)

References 50 publications

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“…• We extensively validate our framework using 44 real experiments in five different areas on our campus, three classrooms, a conference room, and a hallway (see Fig. 6,8,9,11,and 12). More specifically, we show that we can estimate up to and including 20 people with an error of 2 people or less 100% of the time and with an error of 1 person or less 75% of the time.…”

Section: Introductionmentioning

confidence: 84%

Crowd Counting Through Walls Using WiFi

Depatla

Mostofi

2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.

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Section: Introductionmentioning

confidence: 84%

Crowd Counting Through Walls Using WiFi

Depatla

Mostofi

2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…CO 2 has proven a reliable indicator for occupancy detection [10]. Further, CO 2 , illumination and sound are known to be highly correlated with human occupancy [1].…”

Section: Related Workmentioning

confidence: 99%

Using Deep Learning Methods to Monitor Non-Observable States in a Building

Tangrand

Bremdal²

2020

nldl

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This paper presents results from ongoing research with a goal to use a combination of time series from non-intrusive soft sensors and deep recurrent neural networks to predict room usage at a university campus. Training data was created by collecting measurements from sensors measuring room CO2, humidity, temperature, light, motion and sound, while the labels was created manually by human inspection. Results include analyses of relationships between different sensor data sequences and recommendations for a prototype predictive model using deep recurrent neural networks.

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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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Indoor occupancy estimation from carbon dioxide concentration

Cited by 169 publications

References 50 publications

Crowd Counting Through Walls Using WiFi

Crowd Counting Through Walls Using WiFi

Using Deep Learning Methods to Monitor Non-Observable States in a Building

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

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