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

Result code in 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>

Alternative codes found

RIV/61989100:27640/18:10241542

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

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

Publication year

2018

Confidentiality

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

Name of the periodical

IFAC-PapersOnLine. Volume 51

ISSN

2405-8963

e-ISSN

—

Volume of the periodical

51

Issue of the periodical within the volume

6

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

6

Pages from-to

378-383

UT code for WoS article

000445644900064

EID of the result in the Scopus database

2-s2.0-85052901782