Use of the Software PI System Within the Concept of Smart Cities

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238526" target="_blank" >RIV/61989100:27240/17:10238526 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Use of the Software PI System Within the Concept of Smart Cities

Original language description

The article focuses on the description of the possibility of using the PI System software for the monitoring and management of operating and technical functions in Intelligent Buildings (IB) and Smart Home (SH) with the aim of subsequent use for BMS Building Management System) in accordance with the requirements of the concept of Smart Cities (SC). This article describes the implementation of the PI System software for remote monitoring of HVAC control in the office building and monitoring HVAC, lights, and blinds control in the building of residential types. The original contribution of the article is the use of PI System software for large data processing in the real-life operation of buildings in order to efficiently use energy and reduce operational costs in buildings within the concept of SC. In the experimental part of this paper is described the indirect method of predicting CO2 from the measured values of temperature and humidity sensors, using a gradient algorithm for error Backpropagation for the adaptation of a multilayer feed forward neural network. During the realized experiments the Levenberg-Marquardt method, the Bayesian Regulation method and the Regulation Scaled Conjugate gradient method for predicting the course of CO2 concentration (ppm) from the measured indoor temperature T (oC) and relative humidity rH (%) and Outdoor temperature T (oC) were compared. The actual implementation was carried out in an experimental workplace (SH) using BACnet technology components to control heating, cooling, and ventilation. The obtained results were verified and classified using CA (Correlation Analysis) and the RMSE parameters (Root Mean Squared Error) and MAPE (Mean Absolute Percentage Error) were calculated. The information obtained can be used for the indirect monitoring of life activities of residents or for the optimization of technical operating functions. The indirect method for predicting CO2 has the potential to reduce capital and operating costs by reducing the total number of sensors used in intelligent buildings. The experimental results confirm the suitability of the proposed method in real operation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Proceedings of the 9th International Scientific Symposium on Electrical Power Engineering, ELEKTROENERGETIKA 2017

ISBN

978-80-553-3195-9

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

513-517

Publisher name

Technical University of Košice

Place of publication

Košice

Event location

Stará Lesná

Event date

Sep 12, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000431847700100