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Indoor Environment Dataset to Estimate Room Occupancy

Author

Listed:
  • Andreé Vela

    (School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Joanna Alvarado-Uribe

    (School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Hector G. Ceballos

    (School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico)

Abstract

The estimation of occupancy is a crucial contribution to achieve improvements in energy efficiency. The drawback of data or incomplete data related to occupancy in enclosed spaces makes it challenging to develop new models focused on estimating occupancy with high accuracy. Furthermore, considerable variation in the monitored spaces also makes it difficult to compare the results of different approaches. This dataset comprises the indoor environmental information (pressure, altitude, humidity, and temperature) and the corresponding occupancy level for two different rooms: (1) a fitness gym and (2) a living room. The fitness gym data were collected for six days between 18 September and 2 October 2019, obtaining 10,125 objects with a 1 s resolution according to the following occupancy levels: low (2442 objects), medium (5325 objects), and high (2358 objects). The living room data were collected for 11 days between 14 May and 4 June 2020, obtaining 295,823 objects with a 1 s resolution, according to the following occupancy levels: empty (50,978 objects), low (202,613 objects), medium (35,410 objects), and high (6822 objects). Additionally, the number of fans turned on is provided for the living room data. The data are publicly available in the Mendeley Data repository. This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces.

Suggested Citation

  • Andreé Vela & Joanna Alvarado-Uribe & Hector G. Ceballos, 2021. "Indoor Environment Dataset to Estimate Room Occupancy," Data, MDPI, vol. 6(12), pages 1-12, December.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:12:p:133-:d:700931
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    References listed on IDEAS

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    1. Sachin Kumar & Jagvinder Singh & Ompal Singh, 2020. "Ensemble-based extreme learning machine model for occupancy detection with ambient attributes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 173-183, July.
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    Cited by:

    1. Aleksandr Ometov & Joaquín Torres-Sospedra, 2022. "Measurements of User and Sensor Data from the Internet of Things (IoT) Devices," Data, MDPI, vol. 7(5), pages 1-3, April.
    2. Juan Botero-Valencia & Luis Castano-Londono & David Marquez-Viloria, 2022. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments," Data, MDPI, vol. 7(6), pages 1-15, June.

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