Utilization of the LMS Algorithm to Filter the Predicted Course by Means of Neural Networks for Monitoring the Occupancy of Rooms in an Intelligent Administrative Building

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241542" target="_blank" >RIV/61989100:27240/18:10241542 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27640/18:10241542

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2405896318309297" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896318309297</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2018.07.183" target="_blank" >10.1016/j.ifacol.2018.07.183</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Utilization of the LMS Algorithm to Filter the Predicted Course by Means of Neural Networks for Monitoring the Occupancy of Rooms in an Intelligent Administrative Building

Popis výsledku v původním jazyce

For monitoring the occupancy of individual rooms in an intelligent administrative building (IAB), a wide variety of sensors can be used, by means of which the presence of a person in the monitored area can be determined. For a larger administrative-type building, installation of additional sensors means considerable investment costs. As the present-day standard, temperature or also humidity sensors are installed in individual IAB rooms. The paper describes the proposed method for the determination of occupancy of the monitored area by means of prediction of the course of CO2(ppm) from the measured values of humidity rH(%), indoor temperature Ti(oC) and outdoor temperature To(oC), using the gradient algorithm of back-propagation of error for adaptation of the multilayer feedforward Artificial Neural Network (ANN) in the IAB areas with utilization of the Bayesian regularization method (BRM) to obtain information on the occupancy of individual rooms. The LMS algorithm was used to filter the predicted course in order to determine the occupancy of the monitored areas more precisely. The advantage of the proposed method is the utilization of common operating sensors to obtain information on the state of operational-technical functions in the IAB for the purpose of optimum control of the operational-technical functions of the IAB on the basis of predictable needs of persons using the IAB areas. (C) 2018

Název v anglickém jazyce

Utilization of the LMS Algorithm to Filter the Predicted Course by Means of Neural Networks for Monitoring the Occupancy of Rooms in an Intelligent Administrative Building

Popis výsledku anglicky

For monitoring the occupancy of individual rooms in an intelligent administrative building (IAB), a wide variety of sensors can be used, by means of which the presence of a person in the monitored area can be determined. For a larger administrative-type building, installation of additional sensors means considerable investment costs. As the present-day standard, temperature or also humidity sensors are installed in individual IAB rooms. The paper describes the proposed method for the determination of occupancy of the monitored area by means of prediction of the course of CO2(ppm) from the measured values of humidity rH(%), indoor temperature Ti(oC) and outdoor temperature To(oC), using the gradient algorithm of back-propagation of error for adaptation of the multilayer feedforward Artificial Neural Network (ANN) in the IAB areas with utilization of the Bayesian regularization method (BRM) to obtain information on the occupancy of individual rooms. The LMS algorithm was used to filter the predicted course in order to determine the occupancy of the monitored areas more precisely. The advantage of the proposed method is the utilization of common operating sensors to obtain information on the state of operational-technical functions in the IAB for the purpose of optimum control of the operational-technical functions of the IAB on the basis of predictable needs of persons using the IAB areas. (C) 2018

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2018

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnLine. Volume 51

ISSN

2405-8963

e-ISSN

—

Svazek periodika

51

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

378-383

Kód UT WoS článku

000445644900064

EID výsledku v databázi Scopus

2-s2.0-85052901782