A systematic review and comprehensive analysis of building occupancy prediction

Li, Tao; Liu, Xiangyu; Li, Guannan; Wang, Xing; Ma, Jiangqiaoyu; Xu, Chengliang; Mao, Qianjun

doi:10.1016/j.rser.2024.114284

Cited by 13 publications

(3 citation statements)

References 217 publications

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“…The paper in [ 15 ] effectively highlighted the role of IoT technology and hybrid ML algorithms in enhancing the accuracy of building occupancy predictions, which is crucial for energy optimization and sustainability. However, it could have benefited from a deeper analysis of the practical challenges and limitations associated with deploying IoT in real-world settings.…”

Section: Previous Review Papersmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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Section: Previous Review Papersmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Although, many contemporary buildings prioritize air conditioning for maximum occupancy rather than tailoring it to actual usage and needs. A prevalent control strategy for HVAC systems still relies on predetermined or fixed schedules that overlook occupancy patterns [16]. Specifically, significant energy savings without discomfit can be achieved by reducing the set point, especially during idle periods.…”

Section: Introductionmentioning

confidence: 99%

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Borodinecs,

Palcikovskis,

Krumins

et al. 2024

Preprint

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Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

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“…However, many contemporary buildings prioritize air conditioning for maximum occupancy rather than tailoring it to the actual usage and needs. A prevalent control strategy for HVAC systems still relies on predetermined or fixed schedules that overlook occupancy patterns [16]. Specifically, significant energy savings without discomfort can be achieved by reducing the set point, especially during idle periods.…”

Section: Introductionmentioning

confidence: 99%

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Borodinecs,

Palcikovskis,

Krumins

et al. 2024

Clean Technol.

View full text Add to dashboard Cite

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

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A systematic review and comprehensive analysis of building occupancy prediction

Cited by 13 publications

References 217 publications

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

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