Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242080" target="_blank" >RIV/61989100:27240/19:10242080 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/19/6/1407" target="_blank" >https://www.mdpi.com/1424-8220/19/6/1407</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s19061407" target="_blank" >10.3390/s19061407</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Popis výsledku v původním jazyce

Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO1 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO1 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO1 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.

Název v anglickém jazyce

Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care

Popis výsledku anglicky

Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO1 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO1 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO1 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Sensors (Basel, Switzerland)

ISSN

1424-8220

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

28

Strana od-do

—

Kód UT WoS článku

000465520200096

EID výsledku v databázi Scopus

2-s2.0-85063664851