Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building

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Abstract

The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.
Original languageEnglish
Article number287
JournalScientific Data
Volume6
Number of pages11
ISSN2052-4463
DOIs
Publication statusPublished - 26. Nov 2019

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Intelligent buildings
environmental quality
Modality
Count
Sensing
Data-driven
Sensors
Benchmarking
Cover
Impetus
Behavior Modeling
Sensor
Descriptors
Baseline
Northern Europe
benchmarking
Experiments
Experiment
Environmental quality
experiment

Cite this

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title = "Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building",
abstract = "The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.",
author = "Schwee, {Jens Hjort} and Aslak Johansen and J{\o}rgensen, {Bo N{\o}rregaard} and Kj{\ae}rgaard, {Mikkel Baun} and Mattera, {Claudio Giovanni} and Sangogboye, {Fisayo Caleb} and Christian Veje",
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AU - Schwee, Jens Hjort

AU - Johansen, Aslak

AU - Jørgensen, Bo Nørregaard

AU - Kjærgaard, Mikkel Baun

AU - Mattera, Claudio Giovanni

AU - Sangogboye, Fisayo Caleb

AU - Veje, Christian

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N2 - The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.

AB - The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.

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DO - 10.1038/s41597-019-0274-4

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