People occupancy detection and profiling with 3D depth sensors for building energy management

Diraco, Giovanni; Leone, Alessandro; Siciliano, Pietro

doi:10.1016/j.enbuild.2015.01.043

Cited by 68 publications

(23 citation statements)

References 25 publications

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“…To examine the relationship between occupancy and energy load patterns, it is necessary to estimate building occupancy. There are various methods to estimate building occupancy [23], such as sensor networks [24], wireless camera sensor networks [14], radio-frequency identification (RFID) [2], passive infrared sensors [25], and 3D depth sensors [5]. Each method has merits and limitations; sensor fusion is being used to boost the accuracy of occupancy estimation and has shown better performance [23].…”

Section: Methodsmentioning

confidence: 99%

“…In the United States, the building sector is responsible for 40% of energy use, 75% of electricity consumption, and 38% of related carbon dioxide emissions [4]. The building sector not only has great potential for energy saving but such a saving would also result in lowering the cost for companies and individuals [5,6]. The optimization and reduction of building energy consumption are also critical to the national economy as it cuts business operation cost and increases corporate profitability [2].…”

Section: Introductionmentioning

confidence: 99%

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Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study

2020

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Building energy systems are designed to handle both permanent and temporary occupants. Permanent occupants are considered the base energy load while temporary occupants are considered a temporary or additional load. Temporary occupancy is potentially the most difficult to design as the number of temporary occupants varies more significantly than permanent occupants. This case study was designed to investigate the effect of occupancy on energy loads, i.e. the relationship between occupancy and building energy loads. This study estimated the building occupancy by using existing network infrastructure, such as Wi-Fi and wired Ethernet based on the assumption that the number of Wi-Fi connections and the wired Ethernet traffic were used as a proxy for total and stationary occupancy. The relationships were then examined using correlations and regression analyses. The results showed the following: 1. Stationary occupancy was successfully estimated using the network infrastructure; 2. There was a linear relationship between electricity use and total occupancy (and, thus, the use of network infrastructure); 3. Permanent occupants generated a higher impact on the electricity load than the temporary occupants; 4. There was a logarithmic relationship between electricity use and the Ethernet data traffic (a proxy of permanent occupants); and 5. The statistical and qualitative analyses indicated that there was no significant relationship between occupancy and thermal loads, such as cooling and heating loads.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study

2020

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“…Finally, multiple cascades of boosted classifiers were applied for occupant identification. To alleviate privacy issues, Diraco et al [35] presented a computational framework for occupancy detection and profiling based on pure depth data. The Gaussian average background model was applied to estimate the depth distance in crowded scenes.…”

Section: Cameramentioning

confidence: 99%

An Overview of Real-time Occupancy Information Acquisition Method for Demand-driven Building Energy Management

Liu¹,

Luo²,

Zhang³

2019

Proceedings of the Creative Construction Conference 2019

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Efficient building energy management needs to meet both energy savings and user comfort requirements, which relies on the accurate acquisition of occupancy information over time. In order to meet the data requirements of different building application environments, this paper reviews different sensor technologies based on the principle of obtaining occupancy information, and divides them into five categories, namely motion sensors, environmental sensors, radio frequency (RF) sensors, camera, and contextual sensors. A review of the fusion of multiple sensors that have been widely used for building occupancy detection in recent years has also been conducted. The pros and cons of the prior art are discussed in detail, thus future research can better fit the needs of different system control (such as heating, ventilation, and air conditioning (HVAC) and lighting systems) in different types of buildings.

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“…Much research is underway to identify the uncertainty of human behavior in buildings. Most of the research has been based on surveys, but in recent years, there has been an attempt to develop stochastic occupancy profiles for individual building using occupancy sensor (Duarte et al 2013;Wang et al 2016;Diraco et al 2015), Bluetooth positioning (Zhao et al 2014), and random process (Chen et al 2015;O'Neill and Niu 2017). The identification of actual occupancy schedule may contribute to accurate building energy forecasting and occupancy-based control.…”

Section: Uncertainty In Human Behaviormentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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People occupancy detection and profiling with 3D depth sensors for building energy management

Cited by 68 publications

References 25 publications

Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study

Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study

An Overview of Real-time Occupancy Information Acquisition Method for Demand-driven Building Energy Management

Review on stochastic modeling methods for building stock energy prediction

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