Use of partial least squares within the control relevant identification for buildings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00196180" target="_blank" >RIV/68407700:21230/13:00196180 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.conengprac.2012.09.017" target="_blank" >http://dx.doi.org/10.1016/j.conengprac.2012.09.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.conengprac.2012.09.017" target="_blank" >10.1016/j.conengprac.2012.09.017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Use of partial least squares within the control relevant identification for buildings

Popis výsledku v původním jazyce

Climate changes, diminishing world supplies of non-renewable fuels, as well as economic aspects are probably the most significant driving factors of the current effort to save energy. As buildings account for about 40% of global final energy use, efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in an energy and cost effective manner with minimum retrofitting requirements. In such a predictive control approach, dynamic building models are of crucial importance for a good control performance. An algorithm which has not been used in building modeling yet, namely a combination of minimization of multi-step ahead prediction errors and partial least squares will be investigated. Subsequently, two case studies are presented: the first is an artificial model of a building constructed in Trnsys environment, while the second is a real-life case study. The proposed identification algorithm is then

Název v anglickém jazyce

Use of partial least squares within the control relevant identification for buildings

Popis výsledku anglicky

Climate changes, diminishing world supplies of non-renewable fuels, as well as economic aspects are probably the most significant driving factors of the current effort to save energy. As buildings account for about 40% of global final energy use, efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in an energy and cost effective manner with minimum retrofitting requirements. In such a predictive control approach, dynamic building models are of crucial importance for a good control performance. An algorithm which has not been used in building modeling yet, namely a combination of minimization of multi-step ahead prediction errors and partial least squares will be investigated. Subsequently, two case studies are presented: the first is an artificial model of a building constructed in Trnsys environment, while the second is a real-life case study. The proposed identification algorithm is then

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F12%2F1187" target="_blank" >GAP103/12/1187: Identifikace stochastických, nelineárních systémů pro pokročilé řízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Control Engineering Practice

ISSN

0967-0661

e-ISSN

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Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

113-121

Kód UT WoS článku

000311764200012

EID výsledku v databázi Scopus

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