Occupancy Detection in Smart Home Space Using Interoperable Building Automation Technologies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250539" target="_blank" >RIV/61989100:27240/22:10250539 - isvavai.cz</a>

Výsledek na webu

<a href="http://hcisj.com/articles/issue_view.php?wr_id=402&page=1" target="_blank" >http://hcisj.com/articles/issue_view.php?wr_id=402&page=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22967/HCIS.2022.12.047" target="_blank" >10.22967/HCIS.2022.12.047</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Occupancy Detection in Smart Home Space Using Interoperable Building Automation Technologies

Popis výsledku v původním jazyce

To detect whether people are occupying individual rooms in a smart home, a range of sensors and building automation technologies can be employed. For these technologies to function in tandem and exchange useful data in a smart home environment, they must be interoperable. The article presents a new interoperable solution which combines existing decentralized KNX building automation technology with a KNX/LabVIEW software application gateway using visible light communication to track occupancy in a room. The article also describes a novel KNX/IoT software application gateway which uses an MQTT protocol for interoperability between KNX technology and IBM Watson IoT platform. We conducted an experiment with the originally designed solution to detect occupancy in an office room. We used KNX and BACnet building automation technology to produce an interoperable KNX/BACnet hardware gateway which allowed the application of artificial neural network mathematical methods for CO2 waveform prediction. The best results in detecting occupancy in a room were R = 0.9548 (Levenberg-Marquardt algorithm), R = 0.9872 (Bayesian regularization algorithm), and R = 0.8409 (scaled conjugate gradient algorithm), which correspond to the results obtained by other authors and a minimum system prediction accuracy of 96%.

Název v anglickém jazyce

Occupancy Detection in Smart Home Space Using Interoperable Building Automation Technologies

Popis výsledku anglicky

To detect whether people are occupying individual rooms in a smart home, a range of sensors and building automation technologies can be employed. For these technologies to function in tandem and exchange useful data in a smart home environment, they must be interoperable. The article presents a new interoperable solution which combines existing decentralized KNX building automation technology with a KNX/LabVIEW software application gateway using visible light communication to track occupancy in a room. The article also describes a novel KNX/IoT software application gateway which uses an MQTT protocol for interoperability between KNX technology and IBM Watson IoT platform. We conducted an experiment with the originally designed solution to detect occupancy in an office room. We used KNX and BACnet building automation technology to produce an interoperable KNX/BACnet hardware gateway which allowed the application of artificial neural network mathematical methods for CO2 waveform prediction. The best results in detecting occupancy in a room were R = 0.9548 (Levenberg-Marquardt algorithm), R = 0.9872 (Bayesian regularization algorithm), and R = 0.8409 (scaled conjugate gradient algorithm), which correspond to the results obtained by other authors and a minimum system prediction accuracy of 96%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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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

2022

Kód důvěrnosti údajů

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

Název periodika

Human-centric Computing and Information Sciences

ISSN

2192-1962

e-ISSN

2192-1962

Svazek periodika

12

Číslo periodika v rámci svazku

47

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

000869908300001

EID výsledku v databázi Scopus

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