Method for room occupancy detection based on trajectory of indoor climate sensor data

Pedersen, Theis Heidmann; Nielsen, Kasper Ubbe; Petersen, Steffen

doi:10.1016/j.buildenv.2017.01.023

Cited by 112 publications

(41 citation statements)

References 38 publications

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“…As such, researchers have endeavored to rectify the aforementioned limitation by means of algorithmic developments and augmentation with other sensor types. For instance Pedersen, et al [17] have augmented PIR sensors with additional noise, CO2, Volatile Organic Component (VOC), humidity, and temperate sensors to monitor the state of occupancy of a room. In another study, Dodier, et al [18] have proposed a belief network approach to analyze the data coming from a network of PIR sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep-Learning for Occupancy Detection Using Doppler Radar and Infrared Thermal Array Sensors

Abedi¹,

Jazizadeh²

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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An accurate, real-time monitoring of the occupancy state of each space is a necessity for applications such as energy-aware smart buildings. In this paper, we have studied the feasibility of using Doppler Radar Sensors (DRS) and Infrared Thermal Array Sensors (ITA) to build an effective occupancy detection framework. The proposed sensor types are cost-effective and protect the privacy of the occupants. We have utilized Deep Neural Networks (DNN) to analyze the sensor data without any need for specialized feature extraction that is necessary for classical machine learning approaches. The results are indicative of the feasibility and the reliability of using both sensor types for detection of the occupancy state. While a threshold-based approach reached an average accuracy of 84.3% and 86% for the DRS and ITA sensors respectively, DNN models were able to achieve average accuracies of 98.9% and 99.96% for the DRS and ITA sensors respectively, thereby demonstrating the feasibility and success of the proposed framework.

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Section: Literature Reviewmentioning

confidence: 99%

Deep-Learning for Occupancy Detection Using Doppler Radar and Infrared Thermal Array Sensors

Abedi¹,

Jazizadeh²

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…For example, Pedersen et al applied an occupancy detection method using air temperature, humidity, CO2, volatile organic compounds (VOC), passive infrared sensor (PIR), and noise sensors. The experiment was conducted in a simple test room and in a three-room dorm, to detect two occupancy room statuses-occupied or vacant-resulting in a maximum accuracy of 98% and 78%, respectively in the two study areas [38]. Soh [39,40] studied occupancy estimation has been studied using air temperature, RH, CO2, and air pressure.…”

Section: Related Workmentioning

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

103

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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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“…The measurement of CO2 is useful not only for the occupancy detection but, since CO2 is a gas that, after long-time exposure, can cause undesired effects on the occupants, for comfort purposes too. For example, using data from CO2 sensors, in conjunction with building models to solve a CO2 mass balance equation, has been applied to detect occupancy in [19][20][21].…”

Section: Energy Efficiency Environmental Quality and Use Of The Builmentioning

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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Method for room occupancy detection based on trajectory of indoor climate sensor data

Cited by 112 publications

References 38 publications

Deep-Learning for Occupancy Detection Using Doppler Radar and Infrared Thermal Array Sensors

Deep-Learning for Occupancy Detection Using Doppler Radar and Infrared Thermal Array Sensors

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

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

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