Occupancy Estimation in Buildings Based on Infrared Array Sensors Detection

Yuan, Yazhou; Li, Xin; Liu, Zhixin; Guan, Xinping

doi:10.1109/jsen.2019.2943157

Cited by 35 publications

(31 citation statements)

References 39 publications

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“…Furthermore, the TSA is placed in a fixed location, which results in the system not being work at a different sensor location. Other works [7], [10], [21]- [27] that used the TSA to estimate the occupancy also contain similar sensor location adaptation problems.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation

2021

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Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively.

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Section: Related Workmentioning

confidence: 99%

Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation

2021

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“…Sensors inside a smart building can be used to collect data during a specific time interval referring to each room's temperature and indoor humidity. Acceleration sensors on windows and doors inform the BEMS about changing doors' and windows' state [23] and infrared sensors [30] check occupancy in a room. Those data will be sent to the cloud and the management system will act and manage all technical systems with accuracy ensuring a friendly and energy efficient environment inside.…”

Section: A Smart Technology Building Templatementioning

confidence: 99%

“…1000 (12) where: 31,28,31,30,31,30,31,31,30,31,30,31]. Number of days randomly chosen, in each month P j = Light Power in W/m2 for each hour of the month j P p = Parasitic power in W/m2/hour P dj = Power of projection lighting in W/m2 for each hour of the month j F Dji = Daylight Correction Factor for Time i of Month j F Oji = Building Correction Factor for i hour of the month j F Od = Usage correction factor for projection lighting throughout the year.…”

Section: Monitoring Energy Consumption In Existing Buildingsmentioning

confidence: 99%

Energy Efficiency in Smart Buildings: IoT Approaches

2020

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The Internet of Energy (IoE) impacts on smart cities' power sector. IoE is an implementation of the Internet of Things technology (IoT) into distributed energy systems and aims to achieve energy efficiency, to avoid energy wasting, and improve environmental conditions. IoE technology includes, among others, utilizing smart sensors and renewable energy integration. Therefore, the IoE is becoming a legal science tool to serve the purpose of a smart city. In this paper, we refer to the reasons that led the European Union to compile Regulations for facilitating transformation of existing cities, starting from existing buildings, into smart buildings. We propose a smart building template that manages the performance of all technical systems through IoT technology with the view of achieving energy efficiency. In addition, in order to improve the certification of existing buildings, as for energy performance, we propose an automated remote, control method supported by cloud interface. This method minimizes time consuming procedures and stores, on a cloud platform the energy performance of each building, for the purpose of drawing conclusion and applying measures. INDEX TERMS Energy efficiency, energy performance of existing buildings, Internet of Things, smart building template, smart energy management.

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“…The understanding and modeling of occupancy patterns have been identified as key contributors to achieve improvements in energy efficiency [ 1 , 2 ]. Therefore, the International Energy Agency (IEA), in their Annex 66 [ 3 ] and Annex 79 [ 4 ], highlights the need for further explorations in this area.…”

Section: Introductionmentioning

confidence: 99%

“…Some examples of these technologies are video cameras incorporating people counting methods and infrared illuminators, Radio Frequency Identification (RFID) sensors, optical tripwires, Wireless Fidelity (WiFi), and Bluetooth Low Energy (BLE). Although direct methods have proven to be a viable mechanism for measuring occupancy, they present serious disadvantages, such as expensive hardware, privacy issues, intrusiveness, complex processing, and installation feasibility [ 1 , 5 , 8 , 10 , 11 ]. On the other hand, indirect methods derive occupancy information by measuring the effects of human beings in an enclosed environment, these effects can be changes in CO

concentration, temperature, humidity, luminosity, sound levels, among others.…”

Section: Introductionmentioning

confidence: 99%

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Vela

Alvarado-Uribe

Davila

et al. 2020

Sensors

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The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

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Occupancy Estimation in Buildings Based on Infrared Array Sensors Detection

Cited by 35 publications

References 39 publications

Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation

Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation

Energy Efficiency in Smart Buildings: IoT Approaches

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

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