Application of the Artificial intelligence in the energy solving of Building services of intelligent buildings Areas

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F17%3A00317242" target="_blank" >RIV/68407700:21110/17:00317242 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5593/sgem2017H/63" target="_blank" >http://dx.doi.org/10.5593/sgem2017H/63</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/sgem2017H/63" target="_blank" >10.5593/sgem2017H/63</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of the Artificial intelligence in the energy solving of Building services of intelligent buildings Areas

Popis výsledku v původním jazyce

Currently, a great emphasis is put on the environment, energy intensity, and therefore on applications of the renewable energy sources (RES). Putting a large amount of RES into the modern distribution systems represents certain problems we can address, inter alia, the variable energy outputs of RES aggregated together, including their accumulation managed by respective energy management system (EMS). The result may be the optimization unit commitment of RES distributed in the micro-grids of a fictitious intelligent city composed of an intelligent buildings (IB) complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. The underlying basis of EMS is, among other things, the optimization unit commitment of RES distributed in the micro-grid, distant from a fictitious intelligent city composed of an intelligent buildings complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. As an example of the self-organizing neural network used for cluster analysis (CA), we demonstrate its efficiency in the process of identifying type daily energy consumption diagrams of an intelligent buildings complex combined in the electric micro-grid for a typical working day and typical day off based on network’s annual history. The aforementioned type daily diagrams can be used to predict the power consumption.

Název v anglickém jazyce

Application of the Artificial intelligence in the energy solving of Building services of intelligent buildings Areas

Popis výsledku anglicky

Currently, a great emphasis is put on the environment, energy intensity, and therefore on applications of the renewable energy sources (RES). Putting a large amount of RES into the modern distribution systems represents certain problems we can address, inter alia, the variable energy outputs of RES aggregated together, including their accumulation managed by respective energy management system (EMS). The result may be the optimization unit commitment of RES distributed in the micro-grids of a fictitious intelligent city composed of an intelligent buildings (IB) complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. The underlying basis of EMS is, among other things, the optimization unit commitment of RES distributed in the micro-grid, distant from a fictitious intelligent city composed of an intelligent buildings complex. For this purpose, a computer program has been implemented to optimize operating costs to cover energy consumption of intelligent buildings, based on predicted load profiles. As an example of the self-organizing neural network used for cluster analysis (CA), we demonstrate its efficiency in the process of identifying type daily energy consumption diagrams of an intelligent buildings complex combined in the electric micro-grid for a typical working day and typical day off based on network’s annual history. The aforementioned type daily diagrams can be used to predict the power consumption.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

GREEN BUILDINGS TECHNOLOGIES AND MATERIALS

ISBN

978-619-7408-29-4

ISSN

1314-2704

e-ISSN

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Počet stran výsledku

10

Strana od-do

767-776

Název nakladatele

SGEM WORLD SCIENCE

Místo vydání

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Místo konání akce

Vienna

Datum konání akce

27. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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