Modeling and Estimation of the Humans' Effect on the CO&lt;sub&gt;2&lt;/sub&gt; Dynamics Inside a Conference Room

Weekly, Kevin; Bekiaris-Liberis, Nikolaos; Jin, Ming; Bayen, Alexandre M.

doi:10.1109/tcst.2014.2384002

Cited by 34 publications

(11 citation statements)

References 45 publications

(61 reference statements)

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“…In [3], [37]- [41] the indoor CO2 concentration is assumed to be uniform, and in [37]- [40] the length of time horizon l = 1, which implies that indoor CO2 concentration is with Markov property (memoryless property ). However, [42] showed that the gradient of indoor CO2 concentration can be very large, and CO2 emitted by certain occupant cannot be immediately sensed. Therefore, it is unreasonable to simply set l = 1 unless the sampling time is large enough.…”

Section: Indoor Occupancy Estimation From Co2 Datamentioning

confidence: 99%

Indoor occupancy estimation from carbon dioxide concentration

Jiang

Masood

Soh

et al. 2016

Energy and Buildings

169

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This paper presents an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machine (ELM) but is shown to perform better for the occupancy estimation problem. The measured CO2 concentration suffers from serious spikes. We find that pre-smoothing the CO2 data can greatly improve the estimation accuracy. In real applications, however, we cannot obtain the real-time globally smoothed CO2 data. We provide a way to use the locally smoothed CO2 data instead, which is real-time available. We introduce a new criterion, i.e.x-tolerance accuracy, to assess the occupancy estimator. The proposed occupancy estimator was tested in an office room with 24 cubicles and 11 open seats. The accuracy is up to 94 percent with a tolerance of four occupants.

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Section: Indoor Occupancy Estimation From Co2 Datamentioning

confidence: 99%

Indoor occupancy estimation from carbon dioxide concentration

Jiang

Masood

Soh

et al. 2016

Energy and Buildings

169

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“…For example, CO 2 sensor has been deployed to infer the number of people in a zone [5]. Although there is a response time of the sensor to human movements, careful calibration can be done based on PDE framework to achieve good modeling.…”

Section: A Presence Sensor and Occupancy Sensingmentioning

confidence: 99%

Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence

Jin

Jia

Spanos

2017

IEEE Trans. on Mobile Comput.

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Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semisupervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.

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“…For example, CO 2 sensor has been deployed to infer the number of people in a zone [22]. Although there is a response time of the sensor to human movements, careful calibration can be done based on PDE framework to achieve good modeling.…”

Section: A Presence Sensor and Occupancy Sensingmentioning

confidence: 99%

PresenceSense

Jin

Jia

Kang

et al. 2014

Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

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Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.

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Modeling and Estimation of the Humans' Effect on the CO<sub>2</sub> Dynamics Inside a Conference Room

Cited by 34 publications

References 45 publications

Indoor occupancy estimation from carbon dioxide concentration

Indoor occupancy estimation from carbon dioxide concentration

Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence

PresenceSense

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