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Application of a new CO2 prediction method within family house occupancy monitoring

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248279" target="_blank" >RIV/61989100:27240/21:10248279 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/abstract/document/9625007" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9625007</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ACCESS.2021.3130216" target="_blank" >10.1109/ACCESS.2021.3130216</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Application of a new CO2 prediction method within family house occupancy monitoring

  • Original language description

    The article describes the application of Python for verification of a newly designed method of CO2 prediction from measurements of indoor parameters of temperature and relative humidity within occupancy monitoring in real conditions of a family home. The article describes the implementation of non-electric quantities (indoor CO2 concentration, indoor temperature, indoor relative humidity) measurement in five rooms of a family home (living room, kitchen, children&apos;s room, bathroom, bedroom) using Loxone technology sensors. The IBM IoT (Internet Of Things) was used for storing and subsequent processing of the measured values within the time interval of December 22, 2018, to December 31, 2018. The devised method used radial basis function (artificial neural networks (ANN)) mathematical method (implementation in Python environment) to perform accurate predictions. For further increase of the accuracy and reduction of prediction noise from the obtained course of the predicted signal, multiple variations of the LMS adaptive filter algorithm (Sign, Sign-Sign, Sign-Regressor) were used (implemented in the MATLAB SW tool). The accuracy of the newly proposed CO2 concentration prediction method exceeds 95% in the selected experiments.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Access

  • ISSN

    2169-3536

  • e-ISSN

  • Volume of the periodical

    9

  • Issue of the periodical within the volume

    11

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

  • UT code for WoS article

    000749364300001

  • EID of the result in the Scopus database

    2-s2.0-85120045140