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Statements

Subject Item
n2:RIV%2F68407700%3A21720%2F14%3A00219699%21RIV15-MSM-21720___
rdf:type
n18:Vysledek skos:Concept
dcterms:description
Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20-40%20-40% of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today. Besides enormous potential in reduction of energy consumed by heating, ventilation and air conditioning (HVAC) systems brought by such controller, it suffers from a bottleneck being the necessity of having a reliable mathematical model of the building at disposal. By finding a mathematical model appropriate for the MPC, it is meant to obtain such a model that is able to predict the behavior of the building sufficiently accurately for several hours ahead, which is an especially delicate task. This task is getting even more complicated in case of a real-life application. In this paper, we are looking for a reliable model of a huge three-storey office building in Hasselt, Belgium. For parameter estimation, an advanced identification approach is used – its advantage is that it attacks the problem of minimization of multi-step prediction error and in this way, it corresponds to MPC requirements for a good multi-step predictor. Moreover, we discuss not only the identification approach itself but we also focus on accompanying problems with real-operation data acquisition, processing and special treatment which is an indispensable step for achieving satisfactory identification results. The chosen model is now used in real operation with MPC at Hollandsch Huys. Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20-40%20-40% of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today. Besides enormous potential in reduction of energy consumed by heating, ventilation and air conditioning (HVAC) systems brought by such controller, it suffers from a bottleneck being the necessity of having a reliable mathematical model of the building at disposal. By finding a mathematical model appropriate for the MPC, it is meant to obtain such a model that is able to predict the behavior of the building sufficiently accurately for several hours ahead, which is an especially delicate task. This task is getting even more complicated in case of a real-life application. In this paper, we are looking for a reliable model of a huge three-storey office building in Hasselt, Belgium. For parameter estimation, an advanced identification approach is used – its advantage is that it attacks the problem of minimization of multi-step prediction error and in this way, it corresponds to MPC requirements for a good multi-step predictor. Moreover, we discuss not only the identification approach itself but we also focus on accompanying problems with real-operation data acquisition, processing and special treatment which is an indispensable step for achieving satisfactory identification results. The chosen model is now used in real operation with MPC at Hollandsch Huys.
dcterms:title
Towards the Real-life Implementation of MPC for an Office Building: Identification Issues Towards the Real-life Implementation of MPC for an Office Building: Identification Issues
skos:prefLabel
Towards the Real-life Implementation of MPC for an Office Building: Identification Issues Towards the Real-life Implementation of MPC for an Office Building: Identification Issues
skos:notation
RIV/68407700:21720/14:00219699!RIV15-MSM-21720___
n4:aktivita
n15:S n15:P
n4:aktivity
P(ED3.1.00/13.0283), P(GAP103/12/1187), P(GC13-12726J), S
n4:cisloPeriodika
December
n4:dodaniDat
n12:2015
n4:domaciTvurceVysledku
n11:1741462
n4:druhVysledku
n16:J
n4:duvernostUdaju
n8:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
50851
n4:idVysledku
RIV/68407700:21720/14:00219699
n4:jazykVysledku
n17:eng
n4:klicovaSlova
Building modeling; System identification; Model predictive control
n4:klicoveSlovo
n5:Building%20modeling n5:System%20identification n5:Model%20predictive%20control
n4:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n4:kontrolniKodProRIV
[A5A2CCE4FDF9]
n4:nazevZdroje
Applied Energy
n4:obor
n10:BC
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
3
n4:projekt
n19:GC13-12726J n19:GAP103%2F12%2F1187 n19:ED3.1.00%2F13.0283
n4:rokUplatneniVysledku
n12:2014
n4:svazekPeriodika
135
n4:tvurceVysledku
Váňa, Zdeněk Cigler, Jiří Žáčeková, Eva
n4:wos
000345470100006
s:issn
0306-2619
s:numberOfPages
10
n7:doi
10.1016/j.apenergy.2014.08.004
n6:organizacniJednotka
21720