Propagating sensor uncertainty to better infer office occupancy in smart building control

Papatsimpa, Charikleia; Linnartz, Jean-Paul M. G.

doi:10.1016/j.enbuild.2018.08.039

Cited by 18 publications

(13 citation statements)

References 6 publications

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“…BiB has monitored thousands of buildings to identify the most effective ways to improve the building environment in terms of energy efficiency via big data analysis in cloud platform. In [ 15 ], aiming at the problem of highly unreliable sensors and cloud platform in the practical application of energy intelligent buildings, the author proposes a hierarchical probabilistic framework for occupation-based control of intelligent buildings. The method uses hierarchical linkage, where each layer deals with different aspects of the occupancy detection problem in a probabilistic manner.…”

Section: Related Workmentioning

confidence: 99%

FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

Yang

Zhao³

et al. 2023

J Cloud Comp

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With the awakening of health awareness, people are raising a series of health-related requirements for the buildings they live in, with a view to improving their living conditions. In this context, BIM (Building Information Modeling) makes full use of cutting-edge theories and technologies in many domains such as health, environment, and information technology to provide a new way for engineers to design and build various healthy and green buildings. Specifically, sensors are playing an important role in achieving smart building goals by monitoring the surroundings of buildings, objects and people with the help of cloud computing technology. In addition, it is necessary to quickly determine the optimal sensor placement to save energy and minimize the number of sensors for a building, which is a de-trial task for the cloud platform due to the limited number of sensors available and massive candidate locations for each sensor. In this paper, we propose a Fast Sensor Placement Location Optimization approach (FSPLO) to solve the BIM problem in cloud-aided smart buildings. In particular, we quickly filter out the repeated candidate locations of sensors in FSPLO using Locality Sensitive Hashing (LSH) techniques to maintain only a small number of optimized locations for deploying sensors around buildings. In this way, we can significantly reduce the number of sensors used for health and green buildings. Finally, a set of simulation experiments demonstrates the excellent performance of our proposed FSPLO method.

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Section: Related Workmentioning

confidence: 99%

FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

Yang

Zhao³

et al. 2023

J Cloud Comp

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“…In general, thermal comfort levels were improved by 2.5% and energy consumption was reduced around 17.4% in office buildings. Papatsimpa and Linnartz (2018) proposed a layered probabilistic framework for occupancybased control in intelligent buildings, obtaining energy savings up to 30%. Also aiming toward improving occupant's comfort and energy management in smart buildings, Liang et al (2020) studied the performance of LoRa (long-range) Wireless Communication, by making data of environmental parameters continuously available.…”

Section: Devices Used For Monitoring Ieq Parametersmentioning

confidence: 99%

The role of internet of things (IoT) in the assessment and communication of indoor environmental quality (IEQ) in buildings: a review

Broday

Silva

2022

SASBE

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PurposeThe changes brought by Industry 4.0 go beyond transformations in the industrial environment. The increasingly frequent digitization and robotization of activities is not only restricted to the industrial environment, but also to people's daily routine. People spend a large part of their time inside buildings, and maintaining adequate Indoor Environmental Quality (IEQ) is an essential factor for a healthy and productive environment. In this sense, the purpose of this study is to verify how the Internet of Things (IoT) is being used to improve the indoor environment, through sensors that instantly measure the conditions of the environment.Design/methodology/approachThe aim of this paper is to verify, through a literature review, how IoT is being used for building control (for energy saving purposes) and to monitor IEQ conditions inside buildings, in order to provide a better environment for occupants, in terms of health and comfort. By combining keywords in databases, PRISMA method was used to select the articles for analysis, and 91 articles were analyzed.FindingsThe main findings in this research are: (1) the main purpose for applying IoT inside buildings is to reduce energy consumption; (2) there is an interest in developing low-cost sensoring devices with a learning approach; (3) Machine Learning methods are mainly used for energy saving purposes and to learn about occupants' behavior inside buildings, focusing on thermal comfort; (4) sensors in the IoT era are a requirement to help improve people's comfort and well-being.Originality/valueStudies directly correlating IoT and IEQ are limited. This paper emphasises the link between them, through the presentation of recent methods to control the built environment.

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“…The performance metrics that chosen by the authors of the scientific papers using the Hidden Markov Model integrated with sensor devices in smart buildings included: Accuracy [3,10,25,33,70,115,117,120,122,123,125,127,131,133,136,138]; Precision [25,118,128,133,135,137]; Recall [25,118,128,135]; F-Measure [25,81,121,130,133]; Sensitivity and Specificity [25,33,133]; F1 Score [116,133]; Confusion Matrix [116,127,129]; and Correctness [97,118]. In addition to the above-mentioned performance metrics, other methods that were used to assess the performance of the developed methods by the authors of the scientific papers selected and summarized in Table S13 included: a numerical case study highlighting the efficiency of the developed model [134]; thread latency [119]; evaluation of energy savings…”

Section: Unsupervised Learningmentioning

confidence: 99%

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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Propagating sensor uncertainty to better infer office occupancy in smart building control

Cited by 18 publications

References 6 publications

FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

The role of internet of things (IoT) in the assessment and communication of indoor environmental quality (IEQ) in buildings: a review

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

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