New Method for Accurate Prediction of CO2 in the Smart Home

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86098253" target="_blank" >RIV/61989100:27240/16:86098253 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86098253

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7520562" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7520562</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/I2MTC.2016.7520562" target="_blank" >10.1109/I2MTC.2016.7520562</a>

Jazyk výsledku

angličtina

Název v původním jazyce

New Method for Accurate Prediction of CO2 in the Smart Home

Popis výsledku v původním jazyce

This article describes new method for accurate prediction of CO2 in the Smart Home calculated from the temperature and relative humidity in application of the decision tree regression method. The measured data are loaded from the individual BACnet technology sensors by means of the Desigo Insight visualization tool. The individual BACnet technology components are used to control the heating, cooling and ventilation in Smart Home. The measured temperature (T) and humidity (rH) values are then used as input parameters for prediction of CO2 content in the air of selected rooms in the Smart Home by application of decision tree regression. As described in the article, the method can determine the CO2 content with the accuracy of 46.25 ppm. The obtained information can be used for monitoring the residents' life activities, optimizing the technical service system for reduction of the building's operating costs or automation of its responses to changes of the environment or the residents' activities.

Název v anglickém jazyce

New Method for Accurate Prediction of CO2 in the Smart Home

Popis výsledku anglicky

This article describes new method for accurate prediction of CO2 in the Smart Home calculated from the temperature and relative humidity in application of the decision tree regression method. The measured data are loaded from the individual BACnet technology sensors by means of the Desigo Insight visualization tool. The individual BACnet technology components are used to control the heating, cooling and ventilation in Smart Home. The measured temperature (T) and humidity (rH) values are then used as input parameters for prediction of CO2 content in the air of selected rooms in the Smart Home by application of decision tree regression. As described in the article, the method can determine the CO2 content with the accuracy of 46.25 ppm. The obtained information can be used for monitoring the residents' life activities, optimizing the technical service system for reduction of the building's operating costs or automation of its responses to changes of the environment or the residents' activities.

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

2016 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE PROCEEDINGS

ISBN

978-1-4673-9220-4

ISSN

1091-5281

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1333-1337

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Taipei

Datum konání akce

23. 5. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000382523600234