Real-time occupancy detection with physics-informed pattern-recognition machines based on limited CO2 and temperature sensors

Kampezidou, Styliani I.; Ray, Archana Tikayat; Duncan, Scott J.; Balchanos, Michael; Mavris, Dimitri N.

doi:10.1016/j.enbuild.2021.110863

Cited by 33 publications

(17 citation statements)

References 42 publications

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“…In total, 38.70% of the publications use only environmental measures. For instance, Kampezidou et al [ 84 ] have used one CO

and temperature sensor in a 12.96 m

room. Their study proposes an approach that includes a physics-informed pattern-recognition machine (PI-PRM) to detect occupancy, which achieves 97% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“… Algorithm Results [ 25 ] 2 Y Detection NO YES 1 min LTP, NB, CART Accuracy 90.9–93.5% [ 86 ] 10 D Num. People NO YES 15 min Yolo v4, BTM Accuracy 99.5% [ 87 ] 15 D Detection NO YES 1 min LSTM Accuracy 96.8% [ 84 ] 21 D Detection NO YES 5 seg SGF, SURE, PI-PRM Accuracy 97% [ 88 ] 60 D Detection NO YES 5 min SDLM, LSTM, RF, SVM Accuracy 63–70% [ 18 ] 20 D Num. People...…”

Section: Table A1mentioning

confidence: 99%

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Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed.

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“…In total, 38.70% of the publications use only environmental measures. For instance, Kampezidou et al [ 84 ] have used one CO

and temperature sensor in a 12.96 m

room. Their study proposes an approach that includes a physics-informed pattern-recognition machine (PI-PRM) to detect occupancy, which achieves 97% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Mena

Ceballos

Alvarado-Uribe

2022

Sensors

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“…cleaning routines. With regard to COVID management, IAQ monitoring could support the integrated risk assessment of areas based on occupation and activities performed (Kampezidou et al , 2021). Such information might lead to a set of smart measures for promptly reacting to possible new epidemics or crises (Chan et al , 2021; Sigala, 2020; Wut et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

Indoor air quality monitoring and management in hospitality: an overarching framework

Zanni

Mura

Longo

et al. 2022

IJCHM

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Purpose This study aims to provide a comprehensive framework for the study of indoor air quality (IAQ) in hospitality premises. The goal is to identify the drivers of air pollution, both at the exogenous and endogenous level, to generate insights for facility managers. Design/methodology/approach The complexity of hospitality premises requires an integrated approach to properly investigate IAQ. The authors develop an overarching framework encompassing a monitoring method, based on real-time sensors, a technological standard and a set of statistical analyses for the assessment of both IAQ performance and drivers, based on correlation analyses, analysis of variance and multivariate regressions. Findings The findings suggest that the main drivers of IAQ differ depending on the area monitored: areas in contact with the outdoors or with high ventilation rates, such as halls, are affected by outdoor air quality more than guestrooms or fitness areas, where human activities are the main sources of contamination. Research limitations/implications The results suggest that the integration of IAQ indicators into control dashboards would support management decisions, both in defining protocols to support resilience of the sector in a postpandemic world and in directing investments on the premises. This would also address guests’ pressing demands for a broader approach to cleanliness and safety and support their satisfaction and intention to return. Originality/value To the best of the authors’ knowledge, this is the first study developing a comprehensive framework to systematically address IAQ and its drivers, based on a standard and real-time monitoring. The framework has been applied across the longest period of monitoring for a hospitality premise thus far and over an entire hotel facility.

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“…A large amount of recent work on occupancy detection has focused on sensor fusion techniques [54][55][56][57]. These systems generally utilize a variety of low-cost and low-intrusion sensors, such as CO 2 , relative humidity, and ambient noise.…”

Section: Other Detection Methodsmentioning

confidence: 99%

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Jacoby

Tan

Katanbaf

et al. 2021

JSAN

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Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

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Real-time occupancy detection with physics-informed pattern-recognition machines based on limited CO2 and temperature sensors

Cited by 33 publications

References 42 publications

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

Indoor air quality monitoring and management in hospitality: an overarching framework

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

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