Occupancy determination based on time series of CO2 concentration, temperature and relative humidity

Szczurek, Andrzej; Maciejewska, Monika; Pietrucha, Tomasz

doi:10.1016/j.enbuild.2017.04.080

Cited by 65 publications

(45 citation statements)

References 29 publications

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“…Using environmental parameters to sense occupancy information is a popular way to acquire indirect occupancy data, and some studies have used environmental parameters to calculate occupancy information; for example, with a mass conservation equation [44,45] or model occupancy information [20,31], or with machine learning techniques, since the temperature, humidity, and CO2 concentration are highly related to occupancy presence and count. However This study used three feature-based occupancy models to learn occupancy by using both the indoor air parameters and Wi-Fi data.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, the relationship between occupancy behavior and indoor environmental parameters, including lighting use, CO2 concentration, air temperature, and relative humidity (RH), has been established and proven to be useful [15][16][17]. Occupancy estimation with environmental sensing of multiple parameters is a significant trend [18][19][20]. Zhu et al estimated office occupancy with environmental sensing via non-iterative local receptive fields in time and frequency domains with a dataset including CO2, air RH, air temperature, and air pressure.…”

Section: Introductionmentioning

confidence: 99%

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Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

Wang

Chen

Hong

2018

Automation in Construction

108

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In recent years, many occupancy studies have used environmental sensor data (such as carbon dioxide [CO2], air temperature, and relative humidity) or Wi-Fi data to predict building occupancy information. However, the value of a data fusion approach that uses both environmental sensing and Wi-Fi sensing to predict occupancy remains an open question. To answer this question, this study conducted an on-site experiment in one office room in City University of Hong Kong. Three feature-based occupancy models using machine learning algorithms-k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN)-were selected to learn and predict occupancy information. In the model input, the study tested three data groups: environmental parameters only, Wi-Fi data only, and a combination of both. To assess the three occupancy models, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE) indices were utilized. Results showed that when only the environmental parameter data were applied to learn occupancy, the ANN-based occupancy model was more suitable and accurate, and so will be with the combination of environmental parameters and the Wi-Fi data. The SVM model is more suitable and accurate in learning occupancy information with Wi-Fi data. The ANN model is more suitable environment dataset and the combination. On the other hand, the combination of datasets cannot improve accuracy significantly during three days when compared with

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

Wang

Chen

Hong

2018

Automation in Construction

108

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“…Indirect detection methods are less in number but are gaining attention as they make possible to detect presence reusing other variables, in this group we find detection methods based in environmental values such as temperature, humidity, CO 2 , etc. [23][24] [25].…”

Section: State Of the Artmentioning

confidence: 99%

An IoT sensor network to model occupancy profiles for energy usage simulation tools

Saralegui

Antón

Arbelaitz

et al. 2018

2018 Global Internet of Things Summit (GIoTS)

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The development of IoT devices has allowed to install large amounts of sensors in different environments. Consequently, monitoring small houses and entire buildings has become possible. In addition, buildings are one of the biggest energy consumers, so the monitoring of the energy waste, and its sources, is gaining attention. Human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, thus being able to easily monitor such behaviour will bring greater insight in the building usage. In this paper, an IoT sensor network is proposed to model occupancy profiles at room level. Such measurement of users' behaviour along with additional information such as temperature or humidity can be used to develop strategies to save energy, especially regarding heating, ventilating and airconditioning (HVAC) systems. The proposed equipment has been gathering data for some months in a workplace containing several meeting rooms. Four of those rooms were monitored and later analysed to test the validity of the proposed approach. The results show that it is possible to obtain occupancy profiles by using simple IoT equipment.

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“…CO2 [7], lighting [8], PIR [9], Bluetooth [10,11], Wi-Fi [12,13], and so on. However, with development of sensor and information technologies, to improve the accuracy and robustness of occupancy detection and prediction, occupancy estimation with multiply sensors/parameters fusion is a significant trend instead of by a single parameter [14][15][16]. Among data fusion studies, fusing different types of environment sensing, e.g.…”

Section: Introductionmentioning

confidence: 99%

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Wang

Hong

et al. 2019

Building and Environment

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Fusing various sensing data sources is able to improve the accuracy and reliability of building occupancy detection. Efficiently fusing environmental sensors and wireless network signals is seldom studied for its computational and technical challenges. This study aims to propose an integrated model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources to promote computational efficiency. The adaptive lasso model was introduced for the feature extraction and reduction process. To validate the proposed model, an onsite experiment was conducted and two occupancy resolutions, real-time and four-level occupancy resolutions, were compared. The results suggested that eight features among all twelve features are most relevant. The mean absolute error of the selected data features is about 2.18 for real-time occupancy and F1_accuracy is about 84.36% for four-level occupancy.

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Occupancy determination based on time series of CO2 concentration, temperature and relative humidity

Cited by 65 publications

References 29 publications

Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

An IoT sensor network to model occupancy profiles for energy usage simulation tools

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

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