A review of building occupancy measurement systems

Sun, Kailai; Zhao, Qianchuan; Zou, Jianhong

doi:10.1016/j.enbuild.2020.109965

Cited by 128 publications

(64 citation statements)

References 89 publications

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Section: Related Reviewsmentioning

confidence: 99%

“…Other studies are dedicated to review the most common systems considered to detect occupancy in smart buildings [3,6,7,8,9] These works describe the fine-grained occupancy information in terms of spatial-temporal properties, such as presence, location, count, activity, identity, and track. In [6,7,9], most popular occupancy measurement techniques based on sensors, are described and compared. Authors of [3], classify the systems to obtain these properties in terms of: (i) the method, according to the need of wearable devices or based on passive sensors; (ii) the function, that classifies the systems in individualized system, if they have the capability of identifying and tracking individual building occupants, and non-individualized systems, if they only have the ability of providing occupancy information without user identity information or exact location in the building; and (iii) the infrastructure, that distinguishes the occupancy detection systems between explicit systems, whose unique purpose is to measure occupancy in the building, and implicit systems, which provide occupancy information along with another primary function.…”

Section: Related Reviewsmentioning

confidence: 99%

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Occupant Activity Detection in Smart Buildings: A Review

Cardinale

2020

SSRN Journal

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Building management systems (BMS) in smart buildings are supposed to support the optimization of energy and resources consumption, while ensuring basic users' comfort. A common and effective optimizing strategy is to detect, with high accuracy, room occupancies, events, and activities that occur within a building, to accordingly control the energy usage. Several approaches have been implemented to achieve this goal, combining many technologies (e.g., sensor networks, machine learning techniques) as well as new data sources (e.g., sensed data, social networks) allowing to better detect occupant activities. In this context, the purpose of this study is twofold: (i) identify existing solutions related to capturing occupant activities and events to better manage energy usage and provide occupants' comfort, and (ii) pin down the lessons to learn from existing approaches and technologies in order to design better solutions in this regard. We do not pretend to give an exhaustive revision, but throughout this review, we aim at showing that several data can significantly enrich the typology and content of information managed to detect occupant activities and highlight new possibilities in terms of activities diagnosis and analysis to generate more opportunities in optimizing the energy consumption and providing comfort in smart building.

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Section: Related Reviewsmentioning

confidence: 99%

Section: Related Reviewsmentioning

confidence: 99%

Occupant Activity Detection in Smart Buildings: A Review

Cardinale

2020

SSRN Journal

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“…Occupancy information in buildings has a great impact on energy consumption as well as indoor environment quality [15]. Many studies showed that approximately 10%−40% of the energy consumption in buildings can be saved with occupancy information [16].…”

Section: Internal Gains 41 Occupationmentioning

confidence: 99%

Predicting Energy Requirement for Cooling and Heating the Building Located in South West of Algeria (Bechar)

Draoui

Salima

Imine

2020

ARFMTS

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In buildings, many factors influence the energy performance, such as ambient weather conditions, building structure and characteristics, lighting, equipment, occupants and their behavior. This makes the situation complex to accurately predict the building energy consumption and its behavior. The objective of this piece of work is to contribute to the improvement of the energy efficiency of the residential buildings located in Algeria's southern west city of Bechar. In this study, we are trying to calculate the energy consumption for both cooling and heating in Bechar house. The simulation was conducted using TRNSYS program within one year through two different scenarios. The results obtained, revealed that the cooling load reached 12.223,30 KWh per year when the internal loads has been taken into account, the energy requirements for cooling the building present almost 69.51% of the total needs and the demand for heating presents 30.49% per year.

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“…For instance, an indoor occupancy monitoring system could be used to detect human presence, and to count people in target areas. In this regard, Rueda et al [ 4 ], and Sun et al [ 5 ] presented comprehensive reviews of building occupancy estimation and detection systems, mainly based on sensor fusion. Moreover, in the occupancy problem, many works have been proposed to address specific use cases: safety and security [ 6 ], space allocation in commercial building [ 7 ], and energy management [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of a Room-Level Localization System Based on Bluetooth Low Energy Beacons

García-Paterna¹,

Martínez-Sala²,

Sánchez-Aarnoutse³

2021

Sensors

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The ability to locate an object or a person at room-level inside a building or a house could have multiple applications. In this study, we adapt the fingerprint technique using Bluetooth Low Energy to locate the exact room of a person, seeking a simple and low-cost solution. The system is based on BLE beacons deployed at fixed positions and a person carrying a BLE scanner that generates fingerprints from the BLE beacons in coverage. We formulate it as a classification problem where each room is a class; the objective is to estimate the exact room, trying to maximize the area and number of rooms, but also trying to minimize the number of BLE beacons. The room estimation engine is based on a kNN (k-nearest neighbors) classifier. We evaluate the accuracy in two real scenarios and empirically measure the room estimation success related to the number of BLE beacons. As a proof-of-concept, a laptop and a Raspberry Pi are used as BLE scanners to test different hardware. We follow a measurement campaign for several days at different times to evaluate the stability and repeatability of the system. With just a few beacons an accuracy between 70 and 90% is achieved for house and university scenarios.

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A review of building occupancy measurement systems

Cited by 128 publications

References 89 publications

Occupant Activity Detection in Smart Buildings: A Review

Occupant Activity Detection in Smart Buildings: A Review

Predicting Energy Requirement for Cooling and Heating the Building Located in South West of Algeria (Bechar)

Empirical Study of a Room-Level Localization System Based on Bluetooth Low Energy Beacons

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