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Prediction of CO2 concentration in smart homes using IBM SPSS software tool

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10243998" target="_blank" >RIV/61989100:27240/19:10243998 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Prediction of CO2 concentration in smart homes using IBM SPSS software tool

  • Original language description

    The growth of measured data from intelligent buildings in the recent years provides new opportunities for improvements of quality of the indoor environment. This paper examines the possibilities of increasing the accuracy of CO2 predictions in the smart homes using IBM SPSS software tools. The accuracy of this implementation was evaluated at daily and monthly intervals. Radial basis function (Feedforward neural network) was used in order to predict CO2 concentration levels from the measured relative humidity, indoor and outdoor temperatures. Daily interval resulted in the most accurate prediction results. (C) Technical University of Košice 2019.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20200 - Electrical engineering, Electronic engineering, Information engineering

Result continuities

  • Project

    <a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</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

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 10th International Scientific Symposium on Electrical Power Engineering, ELEKTROENERGETIKA 2019

  • ISBN

    978-80-553-3324-3

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    546-550

  • Publisher name

    Technical University of Košice

  • Place of publication

    Košice

  • Event location

    Stará Lesná

  • Event date

    Sep 16, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article