Room-level occupant counts, airflow and CO
            <sub>2</sub>
            data from an office building

Arendt, Krzysztof; Johansen, Aslak; Jôrgensen, Bo Nørregaard; Kjærgaard, Mikkel Baun; Mattera, Claudio Giovanni; Sangogboye, Fisayo Caleb; Schwee, Jens Hjort; Veje, Christian

doi:10.1145/3277868.3277875

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Another modality that is used to detect occupancy is CO 2 data in a room. A number of studies (Rahman and Han, 2017; Zuraimi et al, 2017; Arendt et al, 2018; Wei et al, 2022) employ ML methods to map the CO concentration and occupancy. Finally, studies such as Zou et al (2019) propose sensor fusion, where data from multiple sensing modalities, i.e., RGB camera, and Wi-Fi, are used in tandem to come up with a robust activity detection mechanism.…”

Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

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Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.

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Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

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“…Infrared array, camera, 3D camera, doppler motion, millimeter-wave radio, infrasound, Radio Frequency Identification (RFID), CO2 sensor OPPORTUNITY [130], PPS grouping [165], PAMAP2 [128] [106], [105], [177], [49], [70], [7], [84] Building energy consumption PIR, temperature, humidity, illumination, CO2, accelerometer, gyroscope, magnetic, GPS, proximity, pressure sensor KAG-energydata [17], LBNL Building 59 [96], CU-BEMS [118] [132], [63], [166], [73] Building indoor environmental quality Temperature, humidity, illumination, sound, carbon monoxide (CO), CO2, formaldehyde, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), total volatile organic compounds (TVOC), pressure sensor LANGEVIN [77], CU-BEMS [118], ROBOD [149] [25], [122], [168] 2.5 Smart Building Use Cases Smart buildings make use of IoT and AI to better manage their facilities, e.g. to monitor, control, and optimize the operations of a building.…”

Section: Iot With Ai In Smart Buildingsmentioning

confidence: 99%

Sensing within Smart Buildings: A Survey

2023

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Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things (IoT) devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g. building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.

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“…This study is focus on methods that predict occupancy in a non-intrusive manner for individuals. By leveraging the pre-existing infrastructure within a building, such as temperature sensors, CO2 sensors, HVAC units, surveillance cameras, and gas consumers, we can monitor occupancy without the need for user-installed software [15] , [16] , [17] , [18] , [19] , [20] . Various mathematical experiments have been conducted for occupancy recognition and people detection in Smart Homes, including Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Random Forest (RF), Decision Trees (DT), rule induction, k-means clustering, and K-nearest neighbor algorithm (KNN), all of which have shown promising results [21] .…”

Section: Related Workmentioning

confidence: 99%