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

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

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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Počet stran výsledku

5

Strana od-do

513-517

Název nakladatele

Technical University of Košice

Místo vydání

Košice

Místo konání akce

Stará Lesná

Datum konání akce

12. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000431847700100