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Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments

Author

Listed:
  • Juan Botero-Valencia

    (Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, Colombia
    These authors contributed equally to this work.)

  • Luis Castano-Londono

    (Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, Colombia
    These authors contributed equally to this work.)

  • David Marquez-Viloria

    (Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, Colombia
    These authors contributed equally to this work.)

Abstract

The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:6:p:81-:d:839923
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    References listed on IDEAS

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    1. Meng Yu & Xuejun Zhang & Yang Zhao & Xiaobin Zhang, 2019. "A Novel Passive Method for Regulating Both Air Temperature and Relative Humidity of the Microenvironment in Museum Display Cases," Energies, MDPI, vol. 12(19), pages 1-17, October.
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    4. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
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    Cited by:

    1. Juan Botero-Valencia & Adrian Martinez-Perez & Ruber Hernández-García & Luis Castano-Londono, 2023. "Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements," Data, MDPI, vol. 8(5), pages 1-13, April.

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