Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Wang, Wei; Hong, Tianzhen; Xu, Ning; Xu, Xiaodong; Chen, Jiayu; Shan, Xiaofang

doi:10.1016/j.buildenv.2019.106280

Cited by 30 publications

(20 citation statements)

References 54 publications

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“…As shown in Table 1, camera-based sensors and PIR (Passive Infra-Red) sensors present the best accuracy levels, followed by CO 2 sensors, but they are also affected by detecting and privacy issues [9], such as the Hawthorne effect for camera-based sensors. It mainly causes alterations of behavior when users are aware of being observed and, if ignored, can affect the reliability of collected data [26].…”

Section: Occupancy Detection: Analysis Of Occupancy Monitoring Systemsmentioning

confidence: 99%

“…Occupancy strongly influences use and cleanness of spaces, which in turn are related to well-being, satisfaction, and productivity of users [5,6]. In recent years, a consistent number of studies investigated the segment of the performance gap between expected energy consumptions, defined during the design phase, and actual consumptions, due to human-building interaction and variable occupancy [6][7][8][9][10][11][12][13][14][15][16][17]. However, other promising fields in building management include security, safety, cleanness, and space management.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment

et al. 2021

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This study focuses on calibration and test campaigns of an IoT camera-based sensor system to monitor occupancy, as part of an ongoing research project aiming at defining a Building Management System (BMS) for facility management based on an occupancy-oriented Digital Twin (DT). The research project aims to facilitate the optimization of building operational stage through advanced monitoring techniques and data analytics. The quality of collected data, which are the input for analyses and simulations on the DT virtual entity, is critical to ensure the quality of the results. Therefore, calibration and test campaigns are essential to ensure data quality and efficiency of the IoT sensor system. The paper describes the general methodology for the BMS definition, and method and results of first stages of the research. The preliminary analyses included Indicative Post-Occupancy Evaluations (POEs) supported by Building Information Modelling (BIM) to optimize sensor system planning. Test campaign are then performed to evaluate collected data quality and system efficiency. The method was applied on a Department of Politecnico di Milano. The period of the year in which tests are performed was critical for lighting conditions. In addition, spaces’ geometric features and user behavior caused major issues and faults in the system.

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Section: Occupancy Detection: Analysis Of Occupancy Monitoring Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment

et al. 2021

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“…A recent research uses the WiFi technology to detect and predict building occupancy [182,183]. A fusion-based approach that combines cross-source data related to CO2 concentration, temperature, and Wi-Fi signal indicator is used for building occupancy analysis [184].…”

Section: Occupant Behaviour Modelingmentioning

confidence: 99%

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim

Dong

Ouf

et al. 2020

Building and Environment

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The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

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“…To date, the use of sensors as a part of POEs to monitor existing buildings have been mostly focused on analyzing energy performances and consumptions and indoor environmental quality (IEQ) (Costa et al 2015, Delzendeh et al 2017, Yan et al 2017, Saralegui et al 2018, Demian et al 2018, Rogage et al 2019, Wang et al 2019a, Wang et al 2019b. Recently, applications of sensors to detect occupancy flows have been explored, mainly referring to building energy consumption analyses and predictive models (Diraco et al 2015, Yan et al 2017, Saralegui et al 2018, Rouleau et al 2019, Wang et al 2019b, as presented in Table 1.…”

Section: The Use Of Sensors To Monitor Occupancymentioning

confidence: 99%

“…One of the main strategies implies the combination of more sensors to detect occupancy (Wang et al 2019b). The first advantage is the possibility of installing only a few new sensors and reuse some of existing PIR sensors linked to security detection systems or Wi-Fi connections.…”

Section: The Use Of Sensors To Monitor Occupancymentioning

confidence: 99%

Building Performance and Occupancy Evaluation for Public Building Stock Management: A State of the Art

Giuda¹,

Pellegrini²,

Seghezzi³

2020

Proceedings of International Structural Engineering and Construction

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The research aims at providing a state of the art regarding the use of Post-occupancy evaluations (POEs) to optimize the facility management phase of large building stocks. Building occupancy evaluations are common topics in the field of building energy performance studies, with recent attention on the impacts of user actions and movements on building performances. Most part of the research implied the use of sensors to monitor these aspects, providing huge amounts of data regarding large building stocks. The research provides a literature review about: methods, tools, and existing studies regarding the use of sensors to monitor occupancy values at room level and users’ flows; applications of machine learning (ML) techniques to analyze large amounts of sensor data and provide valuable predictive information on facility management; existing types of ML techniques and related feasibility for the presented purposes. In addition, the paper investigates the integration in a BIM approach to visualize occupancy levels and predictive information in the Information Model. Potential applications to facilitate the optimization of cleaning activities and reorganization of spaces in large building stocks are also explored, investigating the setting of a decision support system for facility managers to handle building management and cleaning activities using predictive information.

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Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Cited by 30 publications

References 54 publications

Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment

Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Building Performance and Occupancy Evaluation for Public Building Stock Management: A State of the Art

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